![]() The training and test labels are 1x4 and 1圆 vectors for SAT-4 and SAT-6 respectively having a single 1 indexing a particular class from 0 through 4 or 6 and 0 values at all other indices. Each sample image is 28x28 pixels and consists of 4 bands - red, green, blue and near infrared. mat files that can be read using the standard load command in MATLAB. Care was taken to avoid interclass overlaps within a selected and labeled image patch. 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. EarthView also supports different maps that show our planet earth in different ways, including seasonal changes of vegetation, snow cover and ocean ice.Review: A Review Of Benchmarking In Photogrammetry And Remote Sensing Awesome project The application supports map and globe views, urban areas, city lights, atmospheric effects, clouds, weather information, local time display and much more. Weather data (temperature, humidity, wind, pressure, etc.).Clouds (internet download of current cloud data).This equates to 1 pixel on your screen equaling 10 kilometers on earth at 100% zoom level. ![]() It supports five different beautiful maps of the earth, starting at 10 km resolution. There are numerous options that allow total customization of all view parameters. It produces colorful, high quality, high resolution images for every screen resolution, even beyond 2560x1600. EarthView is a dynamic desktop wallpaper and screen saver application, which displays beautiful views of the earth with daylight and night shadows.
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