{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/understanding-the-role-of-weather-data-for","title":"Understanding the Role of Weather Data for Earth Surface Forecasting using a ConvLSTM-based Model","arxiv_id":null,"date":"2022-06-20","proceeding":"CVPR2022W 2022 6","authors":["Codruţ-Andrei Diaconu","Sudipan Saha","Stephan Günnemann","Xiao Xiang Zhu"],"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\r\nis 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 (Sentinel2 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\r\nimportant 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.","url_abs":"https://openaccess.thecvf.com/content/CVPR2022W/EarthVision/papers/Diaconu_Understanding_the_Role_of_Weather_Data_for_Earth_Surface_Forecasting_CVPRW_2022_paper.pdf","url_pdf":"https://openaccess.thecvf.com/content/CVPR2022W/EarthVision/papers/Diaconu_Understanding_the_Role_of_Weather_Data_for_Earth_Surface_Forecasting_CVPRW_2022_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"understanding-the-role-of-weather-data-for","repo_url":"https://github.com/dcodrut/weather2land","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"earth-surface-forecasting","task_name":"Earth Surface Forecasting"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"video-prediction","task_name":"Video Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/earth-surface-forecasting-on-earthnet2021","task":"Earth Surface Forecasting","dataset":"EarthNet2021 Extreme Track","model":"Diaconu ConvLSTM","rank_in_archive_order":5,"of":6,"metrics":{"EarthNetScore":"0.2140"},"uses_additional_data":false},{"leaderboard":"/sota/earth-surface-forecasting-on-earthnet2021-iid","task":"Earth Surface Forecasting","dataset":"EarthNet2021 IID Track","model":"Diaconu ConvLSTM","rank_in_archive_order":2,"of":7,"metrics":{"EarthNetScore":"0.3266"},"uses_additional_data":false},{"leaderboard":"/sota/earth-surface-forecasting-on-earthnet2021-ood","task":"Earth Surface Forecasting","dataset":"EarthNet2021 OOD Track","model":"Diaconu ConvLSTM","rank_in_archive_order":2,"of":7,"metrics":{"EarthNetScore":"0.3204"},"uses_additional_data":false},{"leaderboard":"/sota/earth-surface-forecasting-on-earthnet2021-1","task":"Earth Surface Forecasting","dataset":"EarthNet2021 Seasonal Track","model":"Diaconu ConvLSTM","rank_in_archive_order":2,"of":6,"metrics":{"EarthNetScore":"0.2193"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}