Spatial Datasets - Academic Torrents

Inria Aerial Image Labeling Dataset (Dataset)

@article{, title= {Inria Aerial Image Labeling Dataset}, keywords= {}, author= {Emmanuel Maggiori and Yuliya Tarabalka and Guillaume Charpiat and Pierre Alliez}, abstract= {The Inria Aerial Image Labeling addresses a core topic in remote sensing: the automatic pixelwise labeling of aerial imagery. Dataset features: Coverage of 810 km² (405 km² for training and 405 km² for testing) Aerial orthorectified color imagery with a spatial resolution of 0.3 m Ground truth data for two semantic classes: building and not building (publicly disclosed only for the training subset) The images cover dissimilar urban settlements, ranging from densely populated areas (e.g., San Francisco’s financial district) to alpine towns (e.g,. Lienz in Austrian Tyrol). Instead of splitting adjacent portions of the same images into the training and test subsets, different cities are included in each of the subsets. For example, images over Chicago are included in the training set (and not on the test set) and images over San Francisco are included on the test set (and not on the training set). The ultimate goal of this dataset is to assess the generalization power of the techniques: while Chicago imagery may be used for training, the system should label aerial images over other regions, with varying illumination conditions, urban landscape and time of the year. The dataset was constructed by combining public domain imagery and public domain official building footprints. https://i.imgur.com/wAL5IUX.png Citation Emmanuel Maggiori, Yuliya Tarabalka, Guillaume Charpiat and Pierre Alliez. “Can Semantic Labeling Methods Generalize to Any City? The Inria Aerial Image Labeling Benchmark”. IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2017. }, terms= {}, license= {}, superseded= {}, url= {https://project.inria.fr/aerialimagelabeling/} }

North America roads GIS data (Dataset)

@article{, title= {North America roads GIS data}, keywords= {Roads, Speed Limit, PBF, OSM, Geofabrik, GIS, North America}, journal= {}, author= {Geofabrik, OSM}, year= {}, url= {https://download.geofabrik.de/north-america.html}, license= {}, abstract= {}, superseded= {}, terms= {} }

UrbanMapper 3D (Digital Surface Model and Digital Terrain Model) Dataset (Dataset)

@article{, title= {UrbanMapper 3D (Digital Surface Model and Digital Terrain Model) Dataset}, keywords= {}, author= {USSOCOM}, abstract= {Competitors will receive an orthorectified color image, Digital Surface Model (DSM), and Digital Terrain Model (DTM) for each geographic area of interest (AOI). The DSM indicates the height of the earth, with objects such as buildings and trees included. The DTM indicates only the height of the ground. Both should be expected to include some errors, and errors may be expected to be similar in the provisional and sequestered data sets. The difference in the DSM and DTM indicates height of objects above ground. All input files provided are raster GeoTIFF images. Ground truth building labels will also be provided for a subset of the data to be used for training ![](https://i.imgur.com/fnAqq30.png) ![](https://i.imgur.com/vMOXxGr.png)}, terms= {}, license= {}, superseded= {}, url= {https://www.topcoder.com/challenges/db36b53a-c2f3-4899-9698-13e96148ffcd} }

UC Merced Land Use Dataset (Dataset)

@article{, title= {UC Merced Land Use Dataset}, keywords= {}, author= {Yi Yang and Shawn Newsam}, abstract= {This is a 21 class land use image dataset meant for research purposes. There are 100 images for each of the following classes: ``` agricultural airplane baseballdiamond beach buildings chaparral denseresidential forest freeway golfcourse harbor intersection mediumresidential mobilehomepark overpass parkinglot river runway sparseresidential storagetanks tenniscourt ``` Each image measures 256x256 pixels. ![](https://i.imgur.com/dT8q6Qi.png) The images were manually extracted from large images from the USGS National Map Urban Area Imagery collection for various urban areas around the country. The pixel resolution of this public domain imagery is 1 foot. Please cite the following paper when publishing results that use this dataset: Yi Yang and Shawn Newsam, "Bag-Of-Visual-Words and Spatial Extensions for Land-Use Classification," ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS), 2010. Shawn D. Newsam Assistant Professor and Founding Faculty Electrical Engineering & Computer Science University of California, Merced Email: snewsam@ucmerced.edu Web: http://faculty.ucmerced.edu/snewsam This material is based upon work supported by the National Science Foundation under Grant No. 0917069.}, terms= {}, license= {}, superseded= {}, url= {http://vision.ucmerced.edu/datasets/landuse.html} }

New York Taxi Data 2009-2016 in Parquet Fomat (Dataset)

@article{, title= {New York Taxi Data 2009-2016 in Parquet Fomat}, keywords= {}, journal= {}, author= {New York Taxi and Limousine Commission}, year= {}, url= {http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml}, license= {}, abstract= {Trip record data from the Taxi and Limousine Commission (http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml) from January 2009-December 2016 was consolidated and brought into a consistent Parquet format by Ravi Shekhar . Data is released under the New York Open Data Law. }, superseded= {}, terms= {} }

Small Object Dataset (Dataset)

@article{, title= {Small Object Dataset}, keywords= {}, author= {Zheng Ma and Lei Yu and Antoni B. Chan}, abstract= {Images of small objects for small instance detections. Currently four object types are available. ![](http://visal.cs.cityu.edu.hk/wp/wp-content/uploads/smallobject.jpg) We collect four datasets of small objects from images/videos on the Internet (e.g.YouTube or Google). Fly Dataset: contains 600 video frames with an average of 86 ± 39 flies per frame (648×72 @ 30 fps). 32 images are used for training (1:6:187) and 50 images for testing (301:6:600). Honeybee Dataset: contains 118 images with an average of 28 ± 6 honeybees per image (640×480). The dataset is divided evenly for training and test sets. Only the first 32 images are used for training. Fish Dataset: contains 387 frames of video with an average of 56±9 fish per frame (300×410 @ 30 fps). 32 images are used for training (1:3:94) and 65 for testing (193:3:387). Seagull Dataset: contains three high-resolution images (624×964) with an average of 866±107 seagulls per image. The first image is used for training, and the rest for testing. Cite this paper: http://visal.cs.cityu.edu.hk/static/pubs/conf/cvpr15-densdet.pdf }, terms= {}, license= {}, superseded= {}, url= {http://visal.cs.cityu.edu.hk/downloads/smallobjects/} }

The Cars Overhead With Context (COWC) (Dataset)

@article{, title= {The Cars Overhead With Context (COWC)}, journal= {}, author= {}, year= {}, url= {http://gdo-datasci.ucllnl.org/cowc/}, abstract= {The Cars Overhead With Context (COWC) data set is a large set of annotated cars from overhead. It is useful for training a device such as a deep neural network to learn to detect and/or count cars. More information can be obtained by reading our paper here. The dataset has the following attributes: (1) Data from overhead at 15 cm per pixel resolution at ground (all data is EO). (2) Data from six distinct locations: Toronto Canada, Selwyn New Zealand, Potsdam and Vaihingen Germany, Columbus and Utah United States. (3) 32,716 unique annotated cars. 58,247 unique negative examples. (4) Intentional selection of hard negative examples. (5) Established baseline for detection and counting tasks. (6) Extra testing scenes for use after validation. Data can be downloaded from our FTP server. The data includes wide area imagery with annotations as well as precompiled image sets for training/validation of classification and counting. Examples of the precompiled image sets are seen on the right. The dataset and research to create this data was done by members of the Computer Vision group within the Computation Engineering Division at Lawrence Livermore National Laboratory under grant from NA-22 in the Global Security Directorate. No Llamas were harmed in the creation of this set. ![](https://i.imgur.com/0dsvDo0.jpg)}, keywords= {}, terms= {}, license= {}, superseded= {} }

Boston Hubway Data Visualization Challenge Dataset (Dataset)

@article{, title= {Boston Hubway Data Visualization Challenge Dataset}, keywords= {}, journal= {}, author= {Massachusetts Department of Transportation (MassDOT)}, year= {}, url= {http://hubwaydatachallenge.org/}, license= {}, abstract= {The Hubway trip history data includes every trip taken through Nov 2013 ? with date, time, origin and destination stations, plus the bike number and more. Data from 2011/07 through 2013/11 The Hubway trip history data Every time a Hubway user checks a bike out from a station, the system records basic information about the trip. Those anonymous data points have been exported into the spreadsheet. Please note, all private data including member names have been removed from these files. What can the data tell us? The CSV file contains data for every Hubway trip from the system launch on July 28th, 2011, through the end of September, 2012. The file contains the data points listed below for each trip. We've also posed some of the questions you could answer with this dataset - we're sure you.ll have lots more of your own. Duration - Duration of trip. What's the average trip duration for annual members vs. casual users? Start date - Includes start date and time. What are the peak Hubway hours? End date - Includes end date and time. Which days of the week get the most Hubway traffic? Start station - Includes starting station name and number. Which stations are most popular? Which stations make up the most popular origin/destination pairs? End station - Includes ending station name and number. Which stations are the most asymmetric - more trips start there than end there, or vice versa? Are they all at the top of hills? Bike Nr - Includes ID number of bike used for the trip. What does a year in the life of one Hubway bike look like? Member Type - Lists whether user was an Annual or Casual (1 or 3 day) member. Which stations get the most tourist traffic, and which get the most commuters? Zip code - Lists the zip code for annual members only. How far does Hubway really reach? Which community should be the next to get Hubway stations? Birthdate - Lists the year in which annual members were born. Are all of the Hubway rentals at 2:00am by people under 25? Gender - Lists gender for annual members only. Are there different top stations for male vs. female Hubway members?}, tos= {}, superseded= {}, terms= {} }

2013 MassDOT Visualizing Transportation Hackathon Dataset (Dataset)

@article{, title= {2013 MassDOT Visualizing Transportation Hackathon Dataset}, keywords= {}, journal= {}, author= {Massachusetts Department of Transportation (MassDOT)}, year= {2013}, url= {https://www.eventbrite.com/e/massdot-visualizing-transportation-hackathon-tickets-9515375745}, license= {}, abstract= {MassDOT Visualizing Transportation Hackathon, December 2013. Informing the Future of Massachusetts Transportation through Data Analysis and Visualization. Introduction At the MassDOT Visualizing Transportation Hackathon, the Massachusetts Department of Transportation (MassDOT), in partnership with the Mass Big Data Initiative, will release a series of related data sets on travel in Massachusetts and will open a challenge to the public to collaborate around analyzing this data and visualizing resulting insights to help inform the future of transportation in the Commonwealth. We invite participants to explore a collection of transportation data with a specific focus on travel behavior, road-rail comparisons, and the energy, environmental, and social impacts of transportation mode-choice. Background Each day in Massachusetts, travelers throughout the state make individual decisions on how to reach their destinations. Together, the public?s transportation ?mode choice? translates into significant outcomes which impact residents across the Commonwealth in many ways, including through traffic congestion, travel costs, and carbon emissions. To better understand traffic flows throughout the state, MassDOT installed pilot network of roadway sensors to provide real-time traffic management (RTTM) information on three major roadways- 93, I-90, and Rt. 3. Each of these roadway ?corridors? is paralleled by at least one light rail line. Participants are encouraged to show compelling insights from the available data, with a special priority around presenting the differences between driving vs. riding the train through specific corridors. The event is designed to encourage participants to choose their own most compelling ?lens? through which to analyze the data, which may include: travel cost, emissions, time delays, etc. Data The event will draw upon several ?core? datasets covering travel behavior. This will provide a foundation from which comparisons can be made within the data and across additional, related regional datasets. Core datasets: Real-time Traffic Management Data Real-time Traffic Management (RTTM) data is collected by MassDOT via a pilot network of sensors that monitor traffic speed in three major roadway corridors (the ?three corridors?) in the Boston Metro area: I-90, 93, and Rt. 3. Sensors at regular intervals at the road level recognize and report the signals of bluetooth-enabled mobile devices in cars as they travel along roadways, calculating a the vehicle travel speed associated with travel between specific road-segments. No personally identifiable information is collected. Already successfully tested, this initial pilot network is already in the process of expanding statewide throughout 2014. The roadway speed data is then processed and made public in two ways: ? A map display of road speeds available online at the following link: http://www.massdot.state.ma.us/highway/TrafficTravelResources/TrafficInformationMaps.aspx ? A real-time xml feed provides the same processed speed data (not the raw capture data) that feeds the map, and is available through a listed on the MassDOT developer?s page at the following link: http://www.massdot.state.ma.us/DevelopersData.aspx Roadway Volume Data Roadway Volume data provides information on the number of vehicles travelling along the corridors Highway Planned & Unplanned Event Data Highway Planned & Unplanned Event data covers scheduled and emergency roadwork and traffic accidents along major Massachusetts roadways. Commuter Rail Corridor Data Commuter Rail Corridor data includes arrival and departure times of trains, ridership ?load counts?, and fare data.}, tos= {}, superseded= {}, terms= {} }

Outerra Earth Data (Dataset)

@article{, title= {Outerra Earth Data}, keywords= {}, journal= {}, author= {Outerra}, year= {}, url= {http://forum.outerra.com/index.php?topic=2396.0}, license= {}, abstract= {Note: to use Outerra Tech Demo without internet connection, run "outerra.exe -demo" from the command line. At the moment this only works with the demo mode, for full mode you have to have internet connection.}, tos= {}, superseded= {}, terms= {} }

The SFU Mountain Dataset: Semi-Structured Woodland Trails Under Changing Environmental Conditions (Dataset)

@InProceedings{bruce:icra15workshop, author= {Jake Bruce and Jens Wawerla and Richard T. Vaughan}, title= {The SFU Mountain Dataset: Semi-Structured Woodland Trails Under Changing Environmental Conditions}, Booktitle= {Workshop on Visual Place Recognition in Changing Environments at the IEEE International Conference on Robotics and Automation (ICRA'15 workshop), Seattle, WA, USA}, month= {May}, year= {2015}, url= {http://autonomylab.org/sfu-mountain-dataset}, license= {https://creativecommons.org/licenses/by/4.0}, abstract= {Note: If you want only a subset of the data presented here (just the ROS bags, but not the extracted JPEGs and CSVs for example, since there is some duplication), you can set your torrent client to download only the files you are interested in. License: This data is licensed under the Creative Commons - Attribution 4.0 International License: https://creativecommons.org/licenses/by/4.0. We present a novel long-term dataset of semistructured woodland terrain under varying lighting and weather conditions and with changing vegetation, infrastructure, and pedestrian traffic. This dataset is intended to aid the development of field robotics algorithms for long-term deployment in challenging outdoor environments. It includes more than 8 hours of trail navigation, with more available in the future as the environment changes. The data consist of readings from calibrated and synchronized sensors operating at 5 Hz to 50 Hz in the form of color stereo and grayscale monocular camera images, vertical and push-broom laser scans, GPS locations, wheel odometry, inertial measurements, and barometric pressure values. Each traversal covers approximately 4 km across three diverse woodland trail environments, and we have recorded under four different lighting and weather conditions to date: dry; wet; dusk; night. We also provide 383 hand-matched location correspondences between traversals as ground-truth for benchmarking place recognition and mapping algorithms. This paper describes the configuration of the vehicle, the trail environments covered, and the format of the data we provide.}, keywords= {Computer Science, robotics, Burnaby Mountain, Trans-Canada Trail, powerline trail, forest trails, video, lidar, imu, gps, barometric pressure, stereo and monocular cameras}, terms= {} }

NLCD2006 Land Cover (2011 Edition) nlcd_2006_landcover_2011_edition_2014_03_31.zip (Dataset)

@article{, title= {NLCD2006 Land Cover (2011 Edition) nlcd_2006_landcover_2011_edition_2014_03_31.zip}, journal= {}, author= {MRLC}, year= {2011}, url= {http://www.mrlc.gov/nlcd06_data.php}, abstract= {The most recent 2011 Edition of NLCD 2006 land cover layer for the conterminous United States for all pixels. National Land Cover Database 2006 (NLCD2006) is a 16-class land cover classification scheme that has been applied consistently across the conterminous United States at a spatial resolution of 30 meters. NLCD2006 is based primarily on the unsupervised classification of Landsat Enhanced Thematic Mapper+ (ETM+) circa 2006 satellite data. NLCD2006 also quantifies land cover change between the years 2001 to 2006. The NLCD2006 land cover change product was generated by comparing spectral characteristics of Landsat imagery between 2001 and 2006, on an individual path/row basis, using protocols to identify and label change based on the trajectory from NLCD2001 products. It represents the first time this type of 30 meter resolution land cover change product has been produced for the conterminous United States. A formal accuracy assessment of the NLCD2006 land cover change product is planned for 2011. Generation of NLCD2006 products helped to identify some issues in the NLCD2001 land cover and percent developed imperviousness products only (there were no changes to the NLCD2001 percent canopy). These issues were evaluated and corrected, necessitating a reissue of NLCD2001 products (NLCD2001 Version 2.0) as part of the NLCD2006 release. A majority of the NLCD2001 updates occurred in coastal mapping zones where NLCD2001 was published prior to the completion of the National Oceanic and Atmospheric Administration (NOAA) Coastal Change Analysis Program (C-CAP) 2001 land cover products. NOAA C-CAP 2001 land cover has now been seamlessly integrated with NLCD2001 land cover for all coastal zones. NLCD2001 percent developed imperviousness was also updated as part of this process. }, keywords= {Dataset}, terms= {}, license= {}, superseded= {} }

Mnih Massachusetts Roads Dataset (Dataset)

@article{, title = {Mnih Massachusetts Roads Dataset}, journal = {}, year = {2013}, url = {http://www.cs.toronto.edu/~vmnih/data/}, abstract = {"The datasets introduced in Chapter 6 of my PhD thesis are below. See the thesis for more details." }, author = {Volodymyr Mnih}, }

Mnih Massachusetts Building Dataset (Dataset)

@article{, title= {Mnih Massachusetts Building Dataset}, journal= {}, year= {2013}, url= {http://www.cs.toronto.edu/~vmnih/data/}, abstract= {"The datasets introduced in Chapter 6 of my PhD thesis are below. See the thesis for more details." }, author= {Volodymyr Mnih}, keywords= {}, terms= {}, license= {}, superseded= {} }

New York City Taxi Trip Data 2013 (Dataset)

@article{, title = {New York City Taxi Trip Data 2013}, journal = {}, author = { Chris Whong}, year = {2013}, url = {http://chriswhong.com/open-data/foil_nyc_taxi/}, license = {}, abstract = {There are two folders of data, Faredata_2013 and Tripdata_2013. Each folder contains chunks of data in csv format, ranging from ~1.5 to ~2.5 GB in size. Fare data looks like this, showing medallion, hack_license, vendor_id, pickup date/time, payment type, fare, tip amount (look at all those zeros!), tolls, and total. Trip data (the good stuff!) looks like this. Each file has about 14 million rows, and each row contains medallion, hack license, vendor id, rate code, store and forward flag, pickup date/time dropoff date/time, passenger count, trip time in seconds, trip distance, and latitude/longitude coordinates for the pickup and dropoff locations. The possibilities are endless! I smell a tip analysis coming on!} }

New York City Taxi Fare Data 2013 (Dataset)

@article{, title = {New York City Taxi Fare Data 2013}, journal = {}, author = { Chris Whong}, year = {2013}, url = {http://chriswhong.com/open-data/foil_nyc_taxi/}, license = {}, abstract = {There are two folders of data, Faredata_2013 and Tripdata_2013. Each folder contains chunks of data in csv format, ranging from ~1.5 to ~2.5 GB in size. Fare data looks like this, showing medallion, hack_license, vendor_id, pickup date/time, payment type, fare, tip amount (look at all those zeros!), tolls, and total. Trip data (the good stuff!) looks like this. Each file has about 14 million rows, and each row contains medallion, hack license, vendor id, rate code, store and forward flag, pickup date/time dropoff date/time, passenger count, trip time in seconds, trip distance, and latitude/longitude coordinates for the pickup and dropoff locations. The possibilities are endless! I smell a tip analysis coming on!} }

US domestic flights from 1990 to 2009 (Dataset)

@article{, title= {US domestic flights from 1990 to 2009}, journal= {}, author= {US Census Bureau}, year= {2009}, url= {http://www.infochimps.com/datasets/35-million-us-domestic-flights-from-1990-to-2009}, abstract= {Over 3.5 million monthly domestic flight records from 1990 to 2009. Data are arranged as an adjacency list with metadata. Ready for immediate database import and analysis. ##Fields: |Short name|Type |Description| |-|-|-| |Origin|String|Three letter airport code of the origin airport| |Destination|String|Three letter airport code of the destination airport| |Origin City|String|Origin city name| |Destination City|String|Destination city name| |Passengers|Integer|Number of passengers transported from origin to destination| |Seats|Integer|Number of seats available on flights from origin to destination| |Flights|Integer|Number of flights between origin and destination (multiple records for one month, many with flights > 1)| |Distance|Integer|Distance (to nearest mile) flown between origin and destination| |Fly Date|Integer|The date (yyyymm) of flight| |Origin Population|Integer|Origin city's population as reported by US Census| |Destination Population|Integer|Destination city's population as reported by US Census| ##Snippet: MFRRDMMedford, ORBend, OR001156200810200298157730 AMAEKOAmarillo, TXElko, NV124124185819930820296040259 TUSEKOTucson, AZElko, NV112124165819930871139240259 AMAEKOAmarillo, TXElko, NV115124185819940620631541668 ICTEKOWichita, KSElko, NV1001241100719960755288445034 SPSEKOWichita Falls, TXElko, NV1221241105919960314768345034 ##Source(s) 1. US Census Bureau 2. RITA/Transtats, Bureau of Transportation Statistics}, keywords= {Dataset}, terms= {} }

UMN Sarwat Foursquare Dataset (September 2013) (Dataset)

@article{, title= {UMN Sarwat Foursquare Dataset (September 2013)}, journal= {}, author= {Mohamed Sarwat and Justin J. Levandoski and Ahmed Eldawy and Mohamed F. Mokbe}, year= {2013}, url= {http://www-users.cs.umn.edu/~sarwat/foursquaredata/}, license= {}, abstract= {This data set contains 2,153,471 users, 1,143,092 venues, 1,021,970 check-ins, 27,098,490 social connections, and 2,809,581 ratings that users assigned to venues; all extracted from the Foursquare application through the public API. All users information have been anonymized, i.e., users geolocations are also anonymized. Each user is represented by an id, and GeoSpatial location. The same for venues. The data are contained in five files, users.dat, venues.dat, checkins.dat, socialgraph.dat, and ratings.dat. More details about the contents and use of all these files follows. Content of Files * users.dat: consists of a set of users such that each user has a unique id and a geospatial location (latitude and longitude) that represents the user home town location. * venues.dat: consists of a set of venues (e.g., restaurants) such that each venue has a unique id and a geospatial location (lattude and longitude). * checkins.dat: marks the checkins (visits) of users at venues. Each check-in has a unique id as well as the user id and the venue id. * socialgraph.dat: contains the social graph edges (connections) that exist between users. Each social connection consits of two users (friends) represented by two unique ids (first_user_id and second_user_id). * ratings.dat: consists of implicit ratings that quantifies how much a user likes a specific venue. Credits The user must acknowledge the use of the data set in publications resulting from the use of the data set by citing the following papers: * Mohamed Sarwat, Justin J. Levandoski, Ahmed Eldawy, and Mohamed F. Mokbel. LARS*: A Scalable and Efficient Location-Aware Recommender System. in IEEE Transactions on Knowledge and Data Engineering TKDE * Justin J. Levandoski, Mohamed Sarwat, Ahmed Eldawy, and Mohamed F. Mokbel. LARS: A Location-Aware Recommender System. in ICDE 2012}, keywords= {foursquare}, terms= {} }

Massachusetts USGS 15cm Color Ortho Imagery (2008/2009) - JPEG2000 Format (Dataset)

@article{, title= {Massachusetts USGS 15cm Color Ortho Imagery (2008/2009) - JPEG2000 Format}, journal= {}, author= {MassGIS }, year= {2008/2009}, url= {http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-geographic-information-massgis/datalayers/colororthos2008.html}, abstract= {This data was converted from the MassGIS coq2008_15cm_sid data. Converted by Joseph Paul Cohen 2014 In spring 2008, the U.S. Geological Survey, as part of its Boston 133 Cities Urban Area mapping program, contracted for true-color imagery covering the metropolitan Boston area and beyond. Image type for the entire region (more than 1.7 million acres) is 24-bit, 3-band (red, green, blue) natural color. Each band has pixel values ranging 0-255. Pixel resolution is 30 cm., or approximately one foot. Additionally, 30 municipalities participated in the Boston Upgrade of the USGS project; these cities and towns contributed funding for separate flights to produce 4-band (red, green, blue, near-infrared) imagery. Pixel resolution for these images is 15 centimeters (approximately 6 inches). In spring 2009, USGS continued the project and 4-band 30cm imagery was obtained for the remainder of the state. Additionally, 14 municipalities provided funding for 4-band 15cm imagery to cover their communities. This digital orthoimagery can serve a variety of purposes, from general planning, to field reference for spatial analysis, to a tool for data development and revision of vector maps. It can also serve as a reference layer or basemap for myriad applications inside geographic information system (GIS) software. Images are available for download in the MrSID Generation 2 format, at 15:1 lossy compression ratio, 3 bands (RGB), as 1,500 meters × 1,500 meters tiles (based on the 2008/2009 USGS Color Ortho Index coq2008-09_index.pdf tiling scheme; refer to the 8-digit numbers in each tile).}, keywords= {}, terms= {}, license= {}, superseded= {} }

Viking MDIM2.1 Colorized Global Mosaic 232m (Dataset)

@article{, title = {Viking MDIM2.1 Colorized Global Mosaic 232m}, journal = {}, author = {NASA}, year = {2014}, url = {http://astrogeology.usgs.gov/search/details/Mars/Viking/MDIM21/Mars_Viking_MDIM21_ClrMosaic_global_232m/cub}, abstract = {This global image map of Mars has a resolution of 256 pixels/degree (scale approximately 231 m/pixel at the equator). The colorized mosaic was completed by NASA AMES which warped the original Viking colorized mosaic and blended over the lastest black/white mosaic. This mosaic, known as Colorized Mars Digital Image Model (MDIM) 2.1. The original MDIM2.1, replaces two earlier mosaics produced by the USGS from the same set of approximately 4600 Viking Orbiter images. The positional accuracy of features in MDIM 2.1 is estimated to be roughly one pixel (200 m), compared to 3 km for MDIM 2.0 released in 2001 and >6 km for MDIM 1.0 released in 1991. In addition to relatively imprecise geodetic control, the previous mosaics were affected by changing definitions of cartographic parameters (such as the definition of zero longitude), resulting in an overall longitude shift of as much as 0.2° between the early MDIMs and other datasets. The new mosaic uses the most recent coordinate system definitions for Mars. These definitions have been widely adopted by NASA missions and other users of planetary data and are likely to remain in use for a decade or more. As a result, MDIM 2.1 not only registers precisely with data from current missions such as MGS and 2001 Mars Odyssey but will serve as an accurate basemap on which data from future missions can be plotted. The basis for the positional accuracy of MDIM 2.1 is the incorporation of all images in the mosaic into the evolving USGS/RAND global control network of Mars. The primary reason for the greatly improved absolute accuracy of the current version of this network is the incorporation of 1232 globally distributed "ground control points" whose latitude and longitude were constrained to values measured from Mars Orbiter Laser Altimeter (MOLA) data. The globally adjusted MOLA dataset has an absolute horizontal accuracy on the order of 100 m, but individual features in images can probably only be tied to MOLA-derived shaded-relief digital image models with a precision on the order of 200 m. Other, lesser contributors to the accuracy of the control solution and mosaic are the use of MOLA-derived elevations for all 37,652 control points, use of updated timing and orientation data for the Viking Orbiter spacecraft, improved measurements of reseau locations in the images leading to more accurate correction of image distortions, and careful checking and re-measurement of control points with large solution residuals. The mosaic is also orthorectified based on the MOLA elevation data, so that parallax distortions present in the earlier versions are eliminated. The root-mean-squared (RMS) error of the control solution is 16 micrometers (1.4 Viking image pixels, or ~300 m on the ground). Visual inspection of the mosaic indicates that both image-to-image seam mismatches and image-to-MOLA registration errors are less than one pixel almost everywhere, with maximum errors on the order of 4 pixels (1 km) occurring in only a few locations. The cartographic constants used in MDIM 2.1 are those adopted by the IAU/IAG in 2000, which have been adopted by the majority of Mars missions and instrument teams. Coordinates (e.g., of the boundaries and centers of the individual files or map quadrangles making up the mosaic) are given in terms of east longitude and planetocentric latitude. The files in cylindrical (Equirectangular) map projection are also constructed so that lines of the map raster are equally spaced in planetocentric latitude. These files will thus register with other datasets based on planetocentric latitude either as-is or after a simple change of scale, but must be resampled in order to register to datasets based on planetographic latitude. The global mosaic is divided into 30 regions based on the USGS Mars Chart (MC) series of 1:5,000,000-scale printed maps. All regions are available in Equirectangular projection, which is a generalization of the more familiar Simple Cylindrical projection. Quadrangles 2-29 are provided only in Equirectangular, with center latitude of projection 0° this projection is identical to Simple Cylindrical. The polar quadrangles 1 and 30 are available in two Equirectangular sections with center latitudes of projection ±60° and ±75.52248781° respectively, and also as a single file in Polar Stereographic projection. The two Equirectangular sections of the polar quadrangles can be converted to center latitude of projection 0° (or equivalently to Simple Cylindrical projection) by 2:1 and 4:1 enlargement in the sample direction, respectively, after which they can be merged with the lower latitude data. The images used to make MDIM 2.1 were obtained primarily through the red, clear, and minus-blue filters of the Viking Orbiter imaging system, and thus provide a monochromatic view of Mars weighted toward the red end of the visible spectrum. Images were obtained with a wide range of solar incidence angles. It is unfortunately not possible to correct the appearance of both albedo (reflectivity) variations and topographic features for these incidence angle variations simultaneously. The images have therefore been highpass-filtered at a scale of ~50 km to remove regional albedo variations and then normalized so that equal topographic slopes appear with equal contrast everywhere. Photometric processing for MDIM 2.1 incorporates a model of the transmission and scattering of light in the atmosphere that is substantially improved over that used in MDIM 2.0. Residual tonal mismatches between different images after photometric correction were corrected based on a least-squares adjustment of image brightness and contrast. Because of these photometric and cosmetic improvements, it was possible to use a less severe highpass filter than for MDIM 2.0, improving the overall appearance of the mosaic. References available from: http://astrogeology.usgs.gov/maps/mdim-2-1 } }

NLCD2006 Land Cover Change (NLCD2006_landcover_change_pixels_5-4-11_se5.zip) (Dataset)

@article{, title= {NLCD2006 Land Cover Change (NLCD2006_landcover_change_pixels_5-4-11_se5.zip)}, journal= {}, author= {USDA}, year= {}, url= {}, abstract= {Land cover layer containing only those pixels identified as changed between NLCD2001 Land Cover Version 2.0 and NLCD2006 Land Cover products for the conterminous United States.}, keywords= {Dataset, nlcd, usgs}, terms= {}, license= {}, superseded= {} }

NLCD2001 Land Cover (Version 2.0) NLCD2001_landcover_v2_2-13-11.zip (Dataset)

@article{, title= {NLCD2001 Land Cover (Version 2.0) NLCD2001_landcover_v2_2-13-11.zip}, journal= {}, author= {USGS}, year= {}, url= {}, abstract= {Version 2.0 of the 2001 land cover layer for the conterminous United States for all pixels. Updated version of NLCD2001 for direct comparison with NLCD2006}, keywords= {Dataset, nlcd, usgs}, terms= {}, license= {}, superseded= {} }

MOLA Shaded Relief / Colorized Elevation (Dataset)

@article{, title= {MOLA Shaded Relief / Colorized Elevation}, journal= {}, author= {ASU }, year= {}, url= {http://www.mars.asu.edu/data/mola_color/}, abstract= { |Attribute|Value| |--------|--------| |Resolution:|128 ppd| |Scale:|463.1 mpp| |Projection:|Simple cylindrical, -180E to 180E, 90N to -90N, 'ocentric| |Layout:|30 x 30 degree tiles| |Total Size:|46080 x 23040 pixels| |Details:| Shaded relief derived from altimetry, colorized by elevation. 128 ppd/460m. NASA MGS/MOLA.| |Source:| http://pds-geosciences.wustl.edu/missions/mgs/mola.html| |Notes:|Data populated from 88N to 88S| ![](http://i.imgur.com/ew6ssh8.png)}, keywords= {Mars, ASU}, terms= {} }

TNC - Freshwater Ecoregions (Dataset)

@article{, title = {TNC - Freshwater Ecoregions}, journal = {}, author = {The Nature Conservancy}, year = {}, url = {}, abstract = {The Freshwater Ecoregions Of the World (FEOW) provide a global biogeographic regionalization of the Earth's freshwater biodiversity. This version of the FEOW, modified by The Nature Conservancy, includes additional tabular data describing Major Habitat Types (MHTs, similar to terrestrial biomes, but unpublished).You can read more about the FEOW, and obtain the unmodified shapefile at www.feow.org.} }

TNC - Terrestrial Ecoregions (Dataset)

@article{, title= {TNC - Terrestrial Ecoregions}, journal= {}, author= {The Nature Conservancy}, year= {2009}, url= {http://maps.tnc.org/files/metadata/TerrEcos.xml}, abstract= {This is the master spatial data layer for TNC's terrestrial ecoregions of the world, exported from the geodatabase listed above. Note that it includes Mangroves, Inland Water, and Rock and Ice MHTs, although they are not being handled by terrestrial assessments. This layer is based on WWF's ecoregions outside the United States, and loosely based on Bailey's ecoregions (from the USDA Forest Service) within the United States. terr-ecoregions-TNC: tnc_terr_ecoregions.dbf tnc_terr_ecoregions.lyr tnc_terr_ecoregions.prj tnc_terr_ecoregions.sbn tnc_terr_ecoregions.sbx tnc_terr_ecoregions.shp tnc_terr_ecoregions.shp.xml tnc_terr_ecoregions.shx}, keywords= {Dataset}, terms= {} }

TNC - Marine Ecoregions (Dataset)

@article{, title = {TNC - Marine Ecoregions}, journal = {}, author = {The Nature Conservancy}, year = {}, url = {}, abstract = {The Marine Ecoregions Of the World (MEOW) data set is a biogeographic classification of the world's coasts and shelves. The ecoregions nest within the broader biogeographic tiers of Realms and Provinces. Further details about the MEOW system and PDFs of the BioScience paper the comprehensive listing of sources are available from www.worldwildlife.org/MEOW/ and www.nature.org/MEOW.} }

Massachusetts USGS 30cm Color Ortho Imagery (2013) - JPEG2000 Format (Dataset)

@article{, title= {Massachusetts USGS 30cm Color Ortho Imagery (2013) - JPEG2000 Format}, journal= {}, author= {MassGIS }, year= {2013}, url= {http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-geographic-information-massgis/datalayers/colororthos2013.html}, abstract= {In spring 2013, the U.S. Geological Survey contracted for true-color imagery covering three urban areas in Massachusetts as defined by the USGS. Those areas are the metropolitan Boston area (and beyond), the greater Worcester area, and the greater Springfield area. Image type for all of the areas is 24 bit, 4-band (red, green, blue, and near-infrared RGBN) portions of the spectrum. Each band has pixel values ranging 0-255. Pixel resolution is 0.3 meters (30 centimeters), or approximately one foot. This digital orthoimagery can serve a variety of purposes, from general planning, to field reference for spatial analysis, to a tool for data development and revision of vector maps. It can also serve as a reference layer or basemap for myriad applications inside geographic information system (GIS) software. It was created to provide easily accessible geospatial data which is readily available to enhance the capability of Federal, State, and local emergency responders, as well as plan for homeland security efforts. These data also support The National Map. Aerial Acquisition The raw ADS80 image data were collected by Fugro EarthData, Inc. at about 2,896 meters above mean terrain during mid to late April 2013. The source imagery is cloud free, and was acquired in generally leaf-off conditions. Images are available for download in the JPEG2000 format, at a 20:1 compression ratio, 4 bands (RGBN), as 1,500 meters × 1,500 meters tiles. }, keywords= {MassGIS}, terms= {}, license= {}, superseded= {} }

Massachusetts USGS 15cm Color Ortho Imagery (2008/2009) - MrSID Format (Dataset)

@article{, title= {Massachusetts USGS 15cm Color Ortho Imagery (2008/2009) - MrSID Format}, journal= {}, author= {MassGIS }, year= {2008/2009}, url= {http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-geographic-information-massgis/datalayers/colororthos2008.html}, abstract= {In spring 2008, the U.S. Geological Survey, as part of its Boston 133 Cities Urban Area mapping program, contracted for true-color imagery covering the metropolitan Boston area and beyond. Image type for the entire region (more than 1.7 million acres) is 24-bit, 3-band (red, green, blue) natural color. Each band has pixel values ranging 0-255. Pixel resolution is 30 cm., or approximately one foot. Additionally, 30 municipalities participated in the Boston Upgrade of the USGS project; these cities and towns contributed funding for separate flights to produce 4-band (red, green, blue, near-infrared) imagery. Pixel resolution for these images is 15 centimeters (approximately 6 inches). In spring 2009, USGS continued the project and 4-band 30cm imagery was obtained for the remainder of the state. Additionally, 14 municipalities provided funding for 4-band 15cm imagery to cover their communities. This digital orthoimagery can serve a variety of purposes, from general planning, to field reference for spatial analysis, to a tool for data development and revision of vector maps. It can also serve as a reference layer or basemap for myriad applications inside geographic information system (GIS) software. Images are available for download in the MrSID Generation 2 format, at 15:1 lossy compression ratio, 3 bands (RGB), as 1,500 meters × 1,500 meters tiles (based on the 2008/2009 USGS Color Ortho Index coq2008-09_index.pdf tiling scheme; refer to the 8-digit numbers in each tile).}, keywords= {MassGIS}, terms= {}, license= {}, superseded= {} }

Massachusetts 1:5,000 Color Ortho Imagery (2005) - JPEG 2000 Format (Original) (Dataset)

@article{, title= {Massachusetts 1:5,000 Color Ortho Imagery (2005) - JPEG 2000 Format (Original)}, journal= {}, author= {MassGIS }, year= {}, url= {}, abstract= {Overview These medium resolution true color images are considered the new "basemap" for the Commonwealth by MassGIS. The photography for the entire commonwealth was captured in April 2005 when deciduous trees were mostly bare and the ground was generally free of snow. Image type is 4-band (RGBN) natural color (Red, Green, Blue) and Near infrared in 8 bits (values ranging 0-255) per band format. Image horizontal accuracy is +/-3 meters at the 95% confidence level at the nominal scale of 1:5,000. This digital orthoimagery can serve a variety of purposes, from general planning, to field reference for spatial analysis, to a tool for development and revision of vector maps. It can also serve as a reference layer or basemap for myriad applications inside geographic information system (GIS) software. The project was funded by the Executive Office of Environmental Affairs, the Department of Environmental Protection, the Massachusetts Highway Department, and the Department of Public Health.. Production Sanborn LLC of Colorado Springs, CO, performed all work for this project. The source imagery was acquired with a Vexcel Ultracam digital camera at a flying height of 5,070 meters above mean terrain and an approximate pixel resolution of 45 cm. Forward overlap was approximately 60%, except 80% in areas with tall structures (downtown Boston, Worcester, and Springfield), in order to reduce building lean, with sidelap of 33%. The entire state was covered by about 5500 image frames, captured over seven days from April 9 through April 17, 2005. The ground control used to support the mapping was collected by photographic identification of strategic points. The ground control coordinates were collected via GPS ground survey techniques. Aerial Triangulation was performed on softcopy workstations using Intergraph ISAT software for photo measurement and matching. The final bundle adjustment was performed using BINGO 5.2 software. A new digital elevation model was stereo compiled for the entire State from the newly acquired 2005 imagery. The DTM includes mass points, soft breaklines and hard breaklines. The images were ortho-rectified using METRO, Sanborn's proprietary software. Bridges were modeled in 3-D using standard photogrammetric stereo-compilation techniques on softcopy workstations. Sanborn's Metro process rectifies the bridges using the 3-Dimensional model using similar methodologies for correcting the positional accuracy of other ground features. The bridges were uniquely coded and later removed from the final deliverable DTM file. Imagery is georeferenced to Massachusetts State Plane Mainland (Lambert Conformal Conic Projection) NAD83 coordinate system, denominated in meters. Color balancing was performed using METRO_NICE software. The resulting images were mosaicked into one seamless database of imagery and extracted to match the existing MassGIS Orthophoto Index Grid tile layout (each image tile covers 4,000x4,000 meters on the ground.). Images were quality-controlled by Sanborn using Adobe PhotoShop software. Final deliverables included 1/2-meter pixel resolution GeoTiff images with supplementary tfw files and metadata. MassGIS quality assurance included rigorous independent checks of the spatial accuracy using other datasets of significantly higher accuracy, and field work that included the capture of highly accurate GPS points that were compared to the same locations appearing on the deliverables. MassGIS also assessed the visual quality and appearance of the images. Distribution Due to the large size of the original half-meter GeoTIFF images, MassGIS is also making these images available in the compressed MrSID and JPEG 2000 (JP2) formats. Options include images tiled by the orthophoto index as wells as large regional mosaics, which comprise from 26 to 73 ortho index tiles. Users may access the JPG2000 data by free download from the MassGIS ftp server or by ordering the Mosaics and MrSID tiles data on CD or DVD. Details are provided below. Original vs. "Contrast Stretched" Imagery MassGIS has produced a set of "Contrast Stretched" MrSID and JP2 data for users who do not have the software tools to modify the appearance of the original imagery. This second set of compressed data was produced from a set of GeoTIFFs that MassGIS modified with a 2.75 standard deviation linear contrast stretch in Erdas Imagine software. A linear contrast stretch is a simple way to improve the visible contrast of an image by changing the individual values of the pixels in the image. Usually, a contrast stretch is performed only on the display device (screen, printer, etc.), so that the data file values do not change. In this case, the stretched pixel values were saved to the tiffs and the tiffs were used to make the second set of MrSID and JP2 files. MassGIS is making this second set of images available for those whose software does not permit display adjustments, or who simply prefer not to adjust the contrast. These contrast stretched images may help solve some of the problems that some users encountered with getting the original images to look the way they wanted. These new images have a much greater contrast when compared to the originals. The drawback is that the stretch is "fixed", so that you cannot recoup the original pixel values. With the original set of images (GeoTIFF, MrSID, and JP2 formats), the user can achieve the same type of contrast adjustment seen in the second set of imagery and still make use of the full range of data values acquired by the digital cameras. Here are screen shots that compare the same area in the original MrSIDs (with no stretch or modification) and the contrast stretched imagery. To learn how to adjust the appearance of the original imagery to your liking, see the Display Options page pdf format of COQ 2005 Display Options doc format of COQ 2005 Display Options DOC file size 1MB . Free Download Images in the following formats are available for download as 4 km tiles (based on the Ortho Index tiling scheme) JPEG 2000, lossy, at 16:1 compression ratio, 4 bands (RGB and IR).15 MB each. Two sets: From original GeoTIFFs From contrast stretched GeoTIFFs }, keywords= {MassGIS, ortho, massachusetts}, terms= {}, license= {}, superseded= {} }

Massachusetts 1:10,000 Coastal Color Orthophoto Images (1994) - MrSID Format (Dataset)

@article{, title = {Massachusetts 1:10,000 Coastal Color Orthophoto Images (1994) - MrSID Format}, journal = {}, author = {MassGIS}, year = {}, url = {}, abstract = {Overview These color coastal orthophotographs were generated through a cooperative effort between the Massachusetts Coastal Zone Management Office, the NOAA Photogrammetry Division and the National Geodetic Survey. The data covers most of the coastal zone region. Digital orthophoto production was provided by Photo Science Inc. of Gaithersburg Maryland. The data set is tiled identically to the MassGIS black and white orthophotos for both the mainland and island regions (398 images; see the Coastal Color Orthophotos Index datalayer description). Additionally, one more image was created for Noman's Land and is not based on the same index. Needs Title-coqimg Click to see the full size image. Methodology The color aerial photography was captured in September and October of 1994 by the Photogrammetry Division of NOAA. The scale of the original photography is 1:48,000. Differential airborne GPS was used for control. Approximately 31 flight lines were conducted, with the orientation of the flight lines designed to cover the maximum area of shoreline. Approximately 360 were captured. Approximately 16 ground panels were placed in the field and surveyed. Aerotriangulation was conducted by the Photogrammetry Division utilizing analytical stereo plotters. The control was processed using 3 block areas: A) North of Boston, B) Boston south including the Elizabeth Islands, and C) Martha?s Vineyard with Nantucket. Control was developed to provide an accuracy that exceeds NMAS of 1:10,000. In large portions of the area, control exceeds the NMAS for 1:7,000. Diapositives were scanned for a final output resolution of 1.0 meter. Scanning was done to match the diapositives as closely as possible. Bulk radiometric adjustments of the imagery was conducted using Adobe Photoshop "auto levels" to remove the green haze and to stretch the contrast. Mass point and breakline elevations were created and used in the production. Only mass point elevations are available for the area. Elevation data was developed primarily for the purpose of orthorectification, and not for detailed contouring. Images for Martha's Vineyard and Nantucket were originally georeferenced to the Massachusetts State Plane Island Zone coordinate system, but have been projected in ArcInfo to the Mainland Zone for consistency with other MassGIS data layers. These mainland-zone images for the islands became available in June 2001; the images for all other areas were released in February, 1998. The original one-meter images are 48 MB per tile. Two-meter versions of the images, resampled in ArcInfo, are 12 MB each. The tiles are in TIFF format and are accompanied by .tfw header files for georeferencing in GIS software. In addition, versions of the one-meter images in the MrSID format have been created at 30:1 compression with 8 zoom levels. These are available with .sdw header files as individual MrSID images as well as one single mosaic comprising the entire coastline, including Martha's Vineyard and Nantucket. The one-meter SIDs may be downloaded or ordered on DVD (from the Digital Data Products section of the order form). The MrSID mosaic may be purchased on DVD because of file size considerations.} }

THEMIS Night IR 100m Global Mosaic (Dataset)

@article{, title = {THEMIS Night IR 100m Global Mosaic}, journal = {}, author = {NASA }, year = {}, url = {}, abstract = {Version: 8.0 Release Date:June 23rd, 2010 Resolution: 592.75 ppd Scale: 99.7 mpp Projection: Simple cylindrical, 0E to 360E, 60N to 60S, 'ocentric Layout: 60 x 30 degree tiles Total Size: 213391 x 88914 pixels Details: Nighttime thermal infrared (12.57um) mosaic. 593 ppd/100m. NASA Mars Odyssey/THEMIS Notes: Gores are filled with black (value=0).} }

NLCD2006 Land Cover (NLCD2006_landcover_4-20-11_se5.zip) (Dataset)

@article{, title= {NLCD2006 Land Cover (NLCD2006_landcover_4-20-11_se5.zip)}, journal= {}, author= {MRLC}, year= {}, url= {http://www.mrlc.gov/nlcd2006.php}, abstract= {National Land Cover Database 2006 (NLCD2006) is a 16-class land cover classification scheme that has been applied consistently across the conterminous United States at a spatial resolution of 30 meters. NLCD2006 is based primarily on the unsupervised classification of Landsat Enhanced Thematic Mapper+ (ETM+) circa 2006 satellite data. NLCD2006 also quantifies land cover change between the years 2001 to 2006. The NLCD2006 land cover change product was generated by comparing spectral characteristics of Landsat imagery between 2001 and 2006, on an individual path/row basis, using protocols to identify and label change based on the trajectory from NLCD2001 products. It represents the first time this type of 30 meter resolution land cover change product has been produced for the conterminous United States. A formal accuracy assessment of the NLCD2006 land cover change product is planned for 2011. Generation of NLCD2006 products helped to identify some issues in the NLCD2001 land cover and percent developed imperviousness products only (there were no changes to the NLCD2001 percent canopy). These issues were evaluated and corrected, necessitating a reissue of NLCD2001 products (NLCD2001 Version 2.0) as part of the NLCD2006 release. A majority of the NLCD2001 updates occurred in coastal mapping zones where NLCD2001 was published prior to the completion of the National Oceanic and Atmospheric Administration (NOAA) Coastal Change Analysis Program (C-CAP) 2001 land cover products. NOAA C-CAP 2001 land cover has now been seamlessly integrated with NLCD2001 land cover for all coastal zones. NLCD2001 percent developed imperviousness was also updated as part of this process. }, superseded= {}, keywords= {NLCD, 2006, Land Cover}, terms= {}, license= {} }