Remote Sensing: Difference between revisions
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''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.'' |
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* Mapping power grids and solar panel locations. |
* Mapping power grids and solar panel locations. |
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==Background Readings== |
==Background Readings== |
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==Online Courses and Course Materials== |
==Online Courses and Course Materials== |
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* [https://www.earthdatascience.org/courses/ GIS & Remote sensing at Earth Lab, University of Colorado] |
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*[https://eo-college.org/welcome/ EO College, European Space Agency] |
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*Coursera: |
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**[https://www.coursera.org/learn/remote-sensing/home/welcome Remote Sensing Image Acquisition, Analysis and Applications] |
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==Community== |
==Community== |
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*'''EARTHVISION''': A workshop regularly held at computer vision conferences. Website [https://www.grss-ieee.org/earthvision2020/ here]. |
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*'''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]. |
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==Libraries and Tools== |
==Libraries and Tools== |
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== Data == |
== Data == |
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* [https://worldview.earthdata.nasa.gov/ NASA Worldview] |
* [https://worldview.earthdata.nasa.gov/ NASA Worldview] |
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* [https://www.google.com/earth/ Google Earth] (permission needed for research use) |
* [https://www.google.com/earth/ Google Earth] (permission needed for research use) |
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*[https://apps.sentinel-hub.com/eo-browser/ Sentinel Hub] (Sentinel, Landsat, Envisat, etc.) |
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Commercial datasets |
Commercial datasets |
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Latest revision as of 07:38, 9 January 2022
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 Wikipedia page on this topic.
Machine learning can be applied to remote sensing data to infer climate-relevant information. Some selected examples include:[1]
- Mapping power grids and solar panel locations.
- Mapping building footprints.
- Pinpointing occurrences of deforestation.
- Creating an inventory of global greenhouse gas emissions.
Background Readings[edit | edit source]
Online Courses and Course Materials[edit | edit source]
- GIS & Remote sensing at Earth Lab, University of Colorado
- EO College, European Space Agency
- Coursera:
Community[edit | edit source]
- EARTHVISION: A workshop regularly held at computer vision conferences. Website here.
- Space and AI: A conference organized by the ESA-CLAIRE AI Special Interest Group on Space. Website here.
Libraries and Tools[edit | edit source]
Data[edit | edit source]
Satellite imagery datasets[edit | edit source]
High-resolution RGB satellite images (for visual predictions)[edit | edit source]
Public datasets
- United States Geological Survey
- Copernicus (Sentinel satellites)
- NASA Worldview
- Google Earth (permission needed for research use)
- Sentinel Hub (Sentinel, Landsat, Envisat, etc.)
Commercial datasets
- DigitalGlobe (up to 31cm resolution)
- Planet (up to 72cm resolution)
Multispectral satellite images (5-13 visible and infrared bands)[edit | edit source]
Public datasets
Commercial datasets
Hyperspectral satellite images (up to a few hundred visible and infrared bands)[edit | edit source]
Public datasets
See also
- Table 1 of the review "Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context"[2] for comparisons between sources.
General satellite images[edit | edit source]
- Awesome Satellite Imagery Datasets: List of aerial and satellite imagery datasets with annotations for computer vision and deep learning.
Street view datasets[edit | edit source]
References[edit | edit source]
- ↑ "Tackling climate change in the EU". Climate Change and Law Collection.
- ↑ Transon, Julie; d’Andrimont, Raphaël; Maugnard, Alexandre; Defourny, Pierre (2018-01-23). "Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context". Remote Sensing. 10 (3): 157. doi:10.3390/rs10020157. ISSN 2072-4292.