{"date_created":"2023-08-22T14:21:47Z","publication":"36th Conference on Neural Information Processing Systems","oa":1,"type":"conference","month":"11","abstract":[{"text":"Geospatial Information Systems are used by researchers and Humanitarian Assistance and Disaster Response (HADR) practitioners to support a wide variety of important applications. However, collaboration between these actors is difficult due to the heterogeneous nature of geospatial data modalities (e.g., multi-spectral images of various resolutions, timeseries, weather data) and diversity of tasks (e.g., regression of human activity indicators or detecting forest fires). In this work, we present a roadmap towards the construction of a general-purpose neural architecture (GPNA) with a geospatial inductive bias, pre-trained on large amounts of unlabelled earth observation data in a self-supervised manner. We envision how such a model may facilitate cooperation between members of the community. We show preliminary results on the first step of the roadmap, where we instantiate an architecture that can process a wide variety of geospatial data modalities and demonstrate that it can achieve competitive performance with domain-specific architectures on tasks relating to the U.N.'s Sustainable Development Goals.","lang":"eng"}],"extern":"1","date_published":"2022-11-04T00:00:00Z","author":[{"last_name":"Rahaman","full_name":"Rahaman, Nasim","first_name":"Nasim"},{"last_name":"Weiss","first_name":"Martin","full_name":"Weiss, Martin"},{"first_name":"Frederik","full_name":"Träuble, Frederik","last_name":"Träuble"},{"orcid":"0000-0002-4850-0683","first_name":"Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco","last_name":"Locatello"},{"last_name":"Lacoste","first_name":"Alexandre","full_name":"Lacoste, Alexandre"},{"last_name":"Bengio","full_name":"Bengio, Yoshua","first_name":"Yoshua"},{"last_name":"Pal","full_name":"Pal, Chris","first_name":"Chris"},{"full_name":"Li, Li Erran","first_name":"Li Erran","last_name":"Li"},{"first_name":"Bernhard","full_name":"Schölkopf, Bernhard","last_name":"Schölkopf"}],"title":"A general purpose neural architecture for geospatial systems","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2211.02348","open_access":"1"}],"year":"2022","day":"04","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"14215","quality_controlled":"1","publication_status":"submitted","oa_version":"Preprint","conference":{"location":"New Orleans, LA, United States","name":"NeurIPS: Neural Information Processing Systems","end_date":"2022-12-09","start_date":"2022-11-28"},"external_id":{"arxiv":["2211.02348"]},"status":"public","date_updated":"2023-09-13T09:35:59Z","language":[{"iso":"eng"}],"department":[{"_id":"FrLo"}],"article_processing_charge":"No","citation":{"short":"N. Rahaman, M. Weiss, F. Träuble, F. Locatello, A. Lacoste, Y. Bengio, C. Pal, L.E. Li, B. Schölkopf, in:, 36th Conference on Neural Information Processing Systems, n.d.","ama":"Rahaman N, Weiss M, Träuble F, et al. A general purpose neural architecture for geospatial systems. In: <i>36th Conference on Neural Information Processing Systems</i>.","mla":"Rahaman, Nasim, et al. “A General Purpose Neural Architecture for Geospatial Systems.” <i>36th Conference on Neural Information Processing Systems</i>.","chicago":"Rahaman, Nasim, Martin Weiss, Frederik Träuble, Francesco Locatello, Alexandre Lacoste, Yoshua Bengio, Chris Pal, Li Erran Li, and Bernhard Schölkopf. “A General Purpose Neural Architecture for Geospatial Systems.” In <i>36th Conference on Neural Information Processing Systems</i>, n.d.","apa":"Rahaman, N., Weiss, M., Träuble, F., Locatello, F., Lacoste, A., Bengio, Y., … Schölkopf, B. (n.d.). A general purpose neural architecture for geospatial systems. In <i>36th Conference on Neural Information Processing Systems</i>. New Orleans, LA, United States.","ista":"Rahaman N, Weiss M, Träuble F, Locatello F, Lacoste A, Bengio Y, Pal C, Li LE, Schölkopf B. A general purpose neural architecture for geospatial systems. 36th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems.","ieee":"N. Rahaman <i>et al.</i>, “A general purpose neural architecture for geospatial systems,” in <i>36th Conference on Neural Information Processing Systems</i>, New Orleans, LA, United States."}}