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. Review: A Review Of Benchmarking In Photogrammetry And Remote Sensing Awesome project
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