Remote Sensing: Difference between revisions

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Line 1: ''This page is about the applications of machine learning (ML) in the context of remote sensing. For an overview of remote sensing more generally, please see the [https://en.wikipedia.org/wiki/Remote_sensing Wikipedia page] on this topic.'' ~~TODO starting page for remote sensing~~ Machine learning can be applied to remote sensing data to infer climate-relevant information. Some selected examples include:<ref>{{Cite web\|title=Tackling climate change in the EU\|url=http://dx.doi.org/10.1163/9789004322714_cclc_2017-0189-005\|website=Climate Change and Law Collection}}</ref> * '''High-resolution RGB satellite images (for visual predictions)''':▼ ** Public * Mapping power grids and solar panel locations. *** United States Geological Survey▼ * Mapping building footprints. *** Copernicus dataset (Sentinel satellites)▼ * Pinpointing occurrences of deforestation. *** NASA Worldview * [[Greenhouse Gas Emissions Detection\|Creating an inventory of global greenhouse gas emissions]]. Public, but permission needed for research use▼ ==Background Readings== * Google Earth ==Online Courses and Course Materials== Commercial * DigitalGlobe (up to 31cm resolution)▼ * [https://www.earthdatascience.org/courses/ GIS & Remote sensing at Earth Lab, University of Colorado] *** Planet (up to 72cm resolution)▼ [https://eo-college.org/welcome/ EO College, European Space Agency] '''Multispectral satellite images (5-13 visible and infrared bands)''':▼ Coursera: * Public [https://www.coursera.org/learn/remote-sensing/home/welcome Remote Sensing Image Acquisition, Analysis and Applications] * Copernicus dataset (Sentinel satellites) * BigEarthNet dataset (Sentinel satellites) ==Community== Commercial *** Digital Globe '''EARTHVISION''': A workshop regularly held at computer vision conferences. Website [https://www.grss-ieee.org/earthvision2020/ here]. ** Planet '''Space and AI:''' A conference organized by the ESA-CLAIRE AI Special Interest Group on Space. Website [https://claire-ai.org/sig-space/?lang=fr here]. '''Hyperspectral satellite images (up to a few hundred visible and infrared bands)''':▼ See Table 1 of this survey of hyperspectral earth observation satellites for comparisons between sources. ==Libraries and Tools== Public == Data == *** United States Geological Survey (Hyperion data)▼ === Satellite imagery datasets === ▲* '''====High-resolution RGB satellite images (for visual predictions)~~''':~~==== Public datasets ▲*** [https://earthexplorer.usgs.gov/ United States Geological Survey] * [https://www.copernicus.eu/en/access-data Copernicus (Sentinel satellites)] * [https://worldview.earthdata.nasa.gov/ NASA Worldview] ▲** ~~Public,~~[https://www.google.com/earth/ ~~but~~Google Earth] (permission needed for research use) [https://apps.sentinel-hub.com/eo-browser/ Sentinel Hub] (Sentinel, Landsat, Envisat, etc.) Commercial datasets ▲[https://www.digitalglobe.com/ DigitalGlobe] (up to 31cm resolution) ▲[https://www.planet.com/ Planet] (up to 72cm resolution) ▲ '''====Multispectral satellite images (5-13 visible and infrared bands)~~''':~~ ==== Public datasets * [https://www.copernicus.eu/en/access-data Copernicus (Sentinel satellites)] ▲*** ~~Copernicus~~[http://bigearth.net/ ~~dataset~~BigEarthNet (Sentinel satellites)] Commercial datasets * [https://www.digitalglobe.com/ Digital Globe] * [https://www.planet.com/ Planet] ▲* '''====Hyperspectral satellite images (up to a few hundred visible and infrared bands)~~''':~~==== Public datasets ▲*** [https://earthexplorer.usgs.gov/ United States Geological Survey (Hyperion data)] See also * Table 1 of the review "Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context"<ref>{{Cite journal\|last=Transon\|first=Julie\|last2=d’Andrimont\|first2=Raphaël\|last3=Maugnard\|first3=Alexandre\|last4=Defourny\|first4=Pierre\|date=2018-01-23\|title=Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context\|url=http://dx.doi.org/10.3390/rs10020157\|journal=Remote Sensing\|volume=10\|issue=3\|pages=157\|doi=10.3390/rs10020157\|issn=2072-4292}}</ref> for comparisons between sources. ==== General satellite images ==== * [https://github.com/chrieke/awesome-satellite-imagery-datasets Awesome Satellite Imagery Datasets]: List of aerial and satellite imagery datasets with annotations for computer vision and deep learning. === Street view datasets === * [https://www.openstreetmap.org/ OpenStreetMap] == References ==