Multi-modal learning for geospatial vegetation forecasting

arXiv:2303.16198v2 Announce Type: replace-cross
Abstract: The innovative application of precise geospatial vegetation forecasting holds immense potential across diverse sectors, including agriculture, forestry, humanitarian aid, and carbon accounting. To leverage the vast availability of satellite imagery for this task, various works have applied deep neural networks for predicting multispectral images in photorealistic quality. However, the important area of vegetation dynamics has not been thoroughly explored. Our study breaks new ground by introducing GreenEarthNet, the first dataset specifically designed for high-resolution vegetation …

abstract accounting agriculture application arxiv availability carbon cs.cv cs.lg diverse forecasting geospatial however humanitarian images modal multi-modal networks neural networks photorealistic quality satellite type vast