ICML 2025

EcoMapper: Generative Modeling for Climate-Aware Satellite Imagery
Muhammed Goktepe * 1 Amir Hossein Shamseddin * 1 Erencan Uysal 1 Javier Muinelo Monteagudo 1
Lukas Drees 2 Aysim Toker 3 Senthold Asseng 4 Malte von Bloh 4
Abstract
Satellite imagery is essential for Earth observa-
tion, enabling applications like crop yield pre-
diction, environmental monitoring, and climate
change assessment. However, integrating satellite
imagery with climate data remains a challenge,
limiting its utility for forecasting and scenario
analysis. We introduce a novel dataset of 2.9 mil-
lion Sentinel-2 images spanning 15 land cover
types with corresponding climate records, form-
ing the foundation for two satellite image genera-
tion approaches using fine-tuned Stable Diffusion
3 models. The first is a text-to-image generation
model that uses textual prompts with climate and
land cover details to produce realistic synthetic im-
agery for specific regions. The second leverages
ControlNet for multi-conditional image genera-
tion, preserving spatial structures while mapping
climate data or generating time-series to simulate
landscape evolution. By combining synthetic im-
age generation with climate and land cover data,
our work advances generative modeling in remote
sensing, offering realistic inputs for environmen-
tal forecasting and new possibilities for climate
adaptation and geospatial analysis.
1. Introduction
Satellite imagery is a critical resource for a variety of re-
search and industrial applications, ranging from environ-
mental monitoring and climate research (de Ara´ujo et al.,
*Equal contribution 1Technical University of Munich, School of
Computation, Information and Technology, Germany 2University
of Zurich, Department of Mathematical Modeling and Machine
Learning, EcoVision Lab, Switzerland 3Technical University of
Munich, School of Computation, Information and Technology, Dy-
namic Vision and Learning Group, Germany 4Technical University
of Munich, School of Life Scien...