EcoMapper: Generative Modeling for Climate-Aware Satellite Imagery (ICML 2025)

Introduction

EcoMapper is a generative modeling framework designed to produce realistic, climate-aware satellite imagery using advanced diffusion models. This project combines remote sensing with climate data, leveraging a globally distributed dataset of 2.9 million Sentinel-2 images across 15 land cover types and 6 years of monthly data.

EcoMapper features two core capabilities:

• Text-to-Image Generation: Uses Stable Diffusion 3 (SD3) to synthesize satellite images from climate-aware text prompts, incorporating location, time, land cover type, and environmental conditions such as temperature, precipitation, and solar radiation.
• Multi-Conditional Image Generation: Integrates ControlNet to guide generation with both text and image inputs, preserving spatial structures while simulating environmental changes over time.

These models enable:

• The creation of cloud-free synthetic observations for regions affected by persistent coverage
• Simulation of future landscapes under different climate scenarios
• Realistic input generation for forecasting tasks in agriculture, disaster response, and land cover monitoring

By fusing multimodal generative AI with Earth observation data, EcoMapper advances climate-aware modeling and opens up new avenues for scenario analysis, geospatial intelligence, and environmental visualization.

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Framework Overview

Figure 1: Text- and image-conditioned satellite image generation using Stable Diffusion 3 and ControlNet.

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Model Outputs Across Regions

Figure 2: Satellite images generated by fine-tuned models vs. ground truth across diverse locations and land cover types.

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Climate Sensitivity in Generated Imagery

Figure 3: Effect of extreme climate inputs (e.g., dry boreal, humid tropical) on generated satellite imagery for different regions.

Installation

To get started, set up the environment and install the required dependencies by following the steps below:

First, clone the repository to your local machine and then create an environment to install requirements.

git clone https://github.com/maltevb/ecomapper.git
cd Ecomapper
conda create -n ecomapper python=3.10
conda activate ecomapper
pip install -r requirements.txt

Note: For diffusionsat related activity, visit https://github.com/samar-khanna/DiffusionSat and follow the corresponding instructions to create an appropriate environment.

Training

EcoMapper provides different versions for training models with or without LoRA, as well as for single image generation and ControlNet. For each training session, two configuration files are needed: one for model architecture, dataset and training parameters (config_train), and another for the masks used for prompting (config_caption).

Single Image Generation

You can train the model with or without LoRA by selecting the appropriate configuration files.

SD3 without LoRA

To train the model without LoRA, use the following command:

accelerate launch --mixed_precision=fp16 train.py --config_train configs/train/train.yaml --config_caption configs/caption/skip_date_location.json

SD3 with LoRA

To train the model with LoRA, use the following command:

accelerate launch --mixed_precision=fp16 train_lora.py --config_train configs/train/train_lora.yaml --config_caption configs/caption/skip_date_location.json

ControlNet Image Generation

For ControlNet image generation, the process is similar: you can train the model with or without LoRA by selecting the corresponding configuration files.

ControlNet SD3 without LoRA

To train ControlNet without LoRA, use this command:

accelerate launch --mixed_precision=fp16 train_controlnet.py --config_train configs/train/train_controlnet.yaml --config_caption configs/caption/skip_date_location.json

ControlNet SD3 with LoRA

To train ControlNet with LoRA, run this command:

accelerate launch --mixed_precision=fp16 train_controlnet_lora.py --config_train configs/train/train_controlnet_lora.yaml --config_caption configs/caption/skip_date_location.json

Inference

After training, use the validation script to generate results from the trained model. Ensure that you specify the correct model paths in the script.

Inference for ControlNet SD3 without LoRA

python validation/validate_controlnet.py

Inference for ControlNet SD3 with LoRA

python validation/validate_controlnet_lora.py

Computing the metrics (after validation scripts)

Notes for diffsat metrics: To compute clip score, before running batch_compute_metrics.py, insert the clip caption from the json files of the SD3 images to the json file of the diffsat images.

SD3

python validation/batch_compute_metrics.py

ControlNet

python validation/batch_compute_metrics_controlnet.py

Citation

If you find our project helpful, please cite our paper:

@inproceedings{goktepe2025ecomapper,
  title     = {EcoMapper: Generative Modeling for Climate‑Aware Satellite Imagery},
  author    = {G{\"o}ktepe, Muhammed and Shamseddin, Amir Hossein and Uysal, Erencan and Monteagudo, Javier and Drees, Lukas and Toker, Aysim and Asseng, Senthold and von Bloh, Malte},
  booktitle = {Proceedings of the 42nd International Conference on Machine Learning (ICML)},
  year      = {2025},
  month     = {July},
}