Detecting Changes in War-Damaged Urban Areas Using the IR-MAD Method and Sentinel-2 Satellite Data
Detekce změn ve válkou poškozených městských oblastech pomocí metody IR-MAD a satelitních dat Sentinel-2
bakalářská práce (OBHÁJENO)
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Trvalý odkaz
http://hdl.handle.net/20.500.11956/194004Identifikátory
SIS: 272268
Kolekce
- Kvalifikační práce [20091]
Konzultant práce
Svoboda, Jan
Fakulta / součást
Přírodovědecká fakulta
Obor
Sociální geografie a geoinformatika
Katedra / ústav / klinika
Katedra aplikované geoinformatiky a kartografie
Datum obhajoby
4. 9. 2024
Nakladatel
Univerzita Karlova, Přírodovědecká fakultaJazyk
Angličtina
Známka
Výborně
Klíčová slova (česky)
Detekce změn, IR-MAD, Google Earth Engine, GazaKlíčová slova (anglicky)
Detekce změn, IR-MAD, Google Earth Engine, Gaza Abstract This study focuses on Sentinel-2 multispectral data for detecting changes associated with war conflicts in an urban environment in Google Earth Engine (GEE) cloud based platform. The area of interest was chosen to be the Gaza City and its surrounding, which became embroiled in a military conflict in October 2023. The Python scripts have been used to perform analyses and monitor spectral signs over time. The iteratively reweighted multivariate alteration detection (IR-MAD) method, which is based on the comparison of two images, was used to analyze the changes. The resulting raster of changes was validated with very high spatial resolution PlanetScope data. Based on the validation, an overall accuracy of 74% was achieved. As part of the research, a web-based mapping application was created to allow users to view conflict using pre-built tools. Key words : Change Detection, IR-MAD, Google Earth Engine, Gaza, 5
Abstract This study focuses on Sentinel-2 multispectral data for detecting changes associated with war conflicts in an urban environment in Google Earth Engine (GEE) cloud based platform. The area of interest was chosen to be the Gaza City and its surrounding, which became embroiled in a military conflict in October 2023. The Python scripts have been used to perform analyses and monitor spectral signs over time. The iteratively reweighted multivariate alteration detection (IR-MAD) method, which is based on the comparison of two images, was used to analyze the changes. The resulting raster of changes was validated with very high spatial resolution PlanetScope data. Based on the validation, an overall accuracy of 74% was achieved. As part of the research, a web-based mapping application was created to allow users to view conflict using pre-built tools. Key words : Change Detection, IR-MAD, Google Earth Engine, Gaza, 5