Understanding the Role of Weather Data for Earth Surface Forecasting using a ConvLSTM-based Model

Codrut Andrei Diaconu, Sudipan Saha, Stephan Gunnemann, Xiao Xiang Zhu

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

16 Zitate (Scopus)

Abstract

Climate change is perhaps the biggest single threat to humankind and the environment, as it severely impacts our terrestrial surface, home to most of the living species. Inspired by video prediction and exploiting the availability of Copernicus Sentinel-2 images, recent studies have attempted to forecast the land surface evolution as a function of past land surface evolution, elevation, and weather. Further extending this paradigm, we propose a model based on convolutional long short-term memory (ConvLSTM) that is computationally efficient (lightweight), however obtains superior results to the previous baselines. By introducing a ConvLSTM-based architecture to this problem, we can not only ingest the heterogeneous data sources (Sentinel-2 time-series, weather data, and a Digital Elevation Model (DEM)) but also explicitly condition the future predictions on the weather. Our experiments confirm the importance of weather parameters in understanding the land cover dynamics and show that weather maps are significantly more important than the DEM in this task. Furthermore, we perform generative simulations to investigate how varying a single weather parameter can alter the evolution of the land surface. All studies are performed using the EarthNet2021 dataset. The code, additional materials and results can be found at https://github.com/dcodrut/weather2land.

OriginalspracheEnglisch
TitelProceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022
Herausgeber (Verlag)IEEE Computer Society
Seiten1361-1370
Seitenumfang10
ISBN (elektronisch)9781665487399
DOIs
PublikationsstatusVeröffentlicht - 2022
Veranstaltung2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022 - New Orleans, USA/Vereinigte Staaten
Dauer: 19 Juni 202220 Juni 2022

Publikationsreihe

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Band2022-June
ISSN (Print)2160-7508
ISSN (elektronisch)2160-7516

Konferenz

Konferenz2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022
Land/GebietUSA/Vereinigte Staaten
OrtNew Orleans
Zeitraum19/06/2220/06/22

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