A General Purpose Neural Architecture for Geospatial Systems

Autor:	Rahaman, Nasim, Weiss, Martin, Träuble, Frederik, Locatello, Francesco, Lacoste, Alexandre, Bengio, Yoshua, Pal, Chris, Li, Li Erran, Schölkopf, Bernhard
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Computers and Society
Druh dokumentu:	Working Paper
Popis:	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. Comment: Presented at AI + HADR Workshop at NeurIPS 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.02348 Zobrazit plný text záznamu View this record from Arxiv