![]() We chose 28x28 as the window size to maintain a significantly bigger context, and at the same time not to make it as big as to drop the relative statistical properties of the target class conditional distributions within the contextual window. Once labeled, 28x28 non-overlapping sliding window blocks were extracted from the uniform image patch and saved to the dataset with the corresponding label. An image labeling tool developed as part of this study was used to manually label uniform image patches belonging to a particular landcover class. In order to maintain the high variance inherent in the entire NAIP dataset, we sample image patches from a multitude of scenes (a total of 1500 image tiles) covering different landscapes like rural areas, urban areas, densely forested, mountainous terrain, small to large water bodies, agricultural areas, etc. The images consist of 4 bands - red, green, blue and Near Infrared (NIR). The imagery is acquired at a 1-m ground sample distance (GSD) with a horizontal accuracy that lies within six meters of photo-identifiable ground control points. The entire NAIP dataset for CONUS is ~65 terabytes. ![]() The average image tiles are ~6000 pixels in width and ~7000 pixels in height, measuring around 200 megabytes each. We used the uncompressed digital Ortho quarter quad tiles (DOQQs) which are GeoTIFF images and the area corresponds to the United States Geological Survey (USGS) topographic quadrangles. The NAIP dataset consists of a total of 330,000 scenes spanning the whole of the Continental United States (CONUS). Images were extracted from the National Agriculture Imagery Program (NAIP) dataset. Qin Zou, Lihao Ni, Tong Zhang and Qian Wang, Deep learning based feature selection for remote sensing scene classification, IEEE Geoscience and Remote Sensing Letters, vol.This dataset is rather challenging due to the wide diversity of the scene images which are captured under changing seasons and varying weathers, and sampled with different scales. For each category, there are 400 images collected from the Google Earth which are sampled on 4 different scales with 100 images per scale. This dataset contains 2800 remote sensing images which are from 7 typical scene categories - the grass land, forest, farm land, parking lot, residential region, industrial region, and river&lake. Symposium: 100 Years ISPRS - Advancing Remote Sensing Science: Vienna, Austria, 2010 Sun, "Structural high-resolution satellite image indexing". 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.The pixel resolution of this public domain imagery is 1 foot. The images were manually extracted from large images from the USGS National Map Urban Area Imagery collection for various urban areas around the country. There are 100 images for each of the following classes:Īgricultural,airplane,baseballdiamond,beach,buildings,chaparral,denseresidential,forest,freeway,golfcourse,harbor,intersection,mediumresidential,mobilehomepark,overpass,parkinglot,river,runway,sparseresidential,storagetanks,tenniscourt This is a 21 class land use image dataset meant for research purposes. Radiant MLHub Open Library for Earth Observations Machine Learning. WebFlipScreenSaver 0.2.0.1 - Possibly broken Lan-speed-test.portable 4.4.0 Downloads cached for licensed users Util-linux-ng-libintl3 2.14.1 - Possibly broken Spiceworks-Agent-Shell 0.2.23 Downloads cached for licensed usersĬue 3.13.94 Downloads cached for licensed users Sharepoint-online-management-shell 2.12000 Downloads cached for licensed usersĪrcanist 2017.08 - Possibly broken Matchanagrams 1.0 Downloads cached for licensed users Installwindowsimage.powershell 2009.5.11 ĭ4t-addons-accurate-music 16.6.19 Onchrome 1.0.2 Downloads cached for licensed users ** Copyright (c) 2021 Microsoft Corporation ** Visual Studio 2019 Developer Command Prompt v16.9.Review: A Review Of Benchmarking In Photogrammetry And Remote Sensing Awesome project Nice-dcv-server 20 Downloads cached for licensed users Officeribboneditor 4.4.2 - Possibly broken 12.0.0.1 - Possibly brokenĭRKSpider 3.2 - Possibly broken Webswing 2.5.10 Downloads cached for licensed users - Possibly broken for FOSS users (due to original download location changes by vendor) VisualStudioExpress2012TFS 11.0.0.1 - Possibly brokenĭroidFonts 4.0.0 - Possibly broken VisualStudioExpress2012WindowsPhone 11.0.0.1 - Possibly broken VisualStudioExpress2012Windows8 11.0.0.1 - Possibly broken Git-flow-dependencies 0.2 - Possibly broken Wizemo 3.51 Downloads cached for licensed users - Possibly broken for FOSS users (due to original download location changes by vendor)ĬouchPotato 2012.11.04.1 - Possibly broken
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