EcoMapper: Generative Modeling for Climate-Aware Satellite Imagery
Authors: Muhammed Goktepe
Amir hossein Shamseddin
Erencan Uysal
Javier Muinelo Monteagudo
Lukas Drees
Aysim Toker
Senthold Asseng
Malte von Bloh
Venue: ICML 2025 poster (2025)
Keywords: remote sensing
generative modeling
stable diffusion
climate
satellite imagery
computer vision
Primary area: applications->chemistry_physics_and_earth_sciences
TL;DR: We present a novel dataset and two generative approaches using fine-tuned Stable Diffusion 3 models to create realistic satellite imagery conditioned on climate and land cover data, enabling single-image and conditional time-series generation.

Abstract: Satellite imagery is essential for Earth observation, enabling applications like crop yield prediction, 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 million Sentinel-2 images spanning 15 land cover types with corresponding climate records, forming the foundation for two satellite image generation 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 imagery for specific regions. The second leverages ControlNet for multi-conditional image generation, preserving spatial structures while mapping climate data or generating time-series to simulate landscape evolution. By combining synthetic image generation with climate and land cover data, our work advances generative modeling in remote sensing, offering realistic inputs for environmental forecasting and new possibilities for climate adaptation and geospatial analysis.

EcoMapper: Generative Modeling for Climate-Aware Satellite Imagery
Muhammed Goktepe" ! Amir Hossein Shamseddin Erencan Uysal' | Javier Muinelo Monteagudo'
Lukas Drees? Aysim Toker Senthold Asseng* Malte von Bloh
Abstract
2025; Hussain et al., 2022) to agriculture (Khanal al., 2020), archaeology (Marín-Buut Buzón et al., 2021), resource exploration (Shirmard et al., 2022), and human develop- ment mapping (Burke et al., 2021b). It provides valuable spatial and temporal data, often serving as the primary or supplementary source for prediction and analysis models, including crop yield forecasting (Khaki al.. 2021: Mu- ruganantham et al., 2022; von Bloh et al., 2023), forestry surveillance (Fassnacht et al., 2024), and disaster manage- ment (Burke et al., 2021a). In many regions of the world, satellite data is the only feasible means of acquiring near real-time information about environmental conditions.
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 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 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.
However, the use of remote sensing hampered by sig- nificant challenges, including cloud coverage, atmospheric distortions, and temporal resolution constraints (Dubovik et al., 2021). Cloud coverage, in particular, renders satellite imagery unusable for large parts of the world, disrupting satellite observations in sloud-prone areas for days or even weeks, depending on the season (King et al., 2013; 33; Mercury et al., 2012). These operational challenges not only hinder real-time monitoring but also raise a critical conceptual gap: the need to integrate satellite imagery with future climate scenarios to enhance prediction accuracy. While various datasets support machine learning applications to address these challenges, most are task-specific or regionally con- strained, limiting their generalizability (Clasen et al., 2024; Christie et al., 2018; Schneider et al., 2023; Van Etten et al., 2018). To address this, we introduce a comprehensive re- mote sensing dataset-one of the largest over 2.9 million Sentinel-2 RGB images linked with cli- mate data, enabling more robust and scalable applications across diverse environmental conditions. Recent advances in multimodal foundation models for remote sensing have significantly improved generation of synthetic satellite im- agery across domains (Hong et al., 2024; Khanna al., 2024; Liu et al., 2024a; Yu et al., 2024b). But key gap remains in generative models capable of producing location- specific satellite imagery conditioned on future climatic conditions. This limitation hinders predictive applications such as seasonal crop yield forecasting and the assessment of climate change impacts on land cover (lizumi et al., 2018; Zachow et al., 2024). These models rely on detailed weather and climate projections improve their accuracy, but often
1. Introduction
fate-feattring
Satellite imagery is a critical resource for a variety of re- search and industrial applications, ranging from environ- mental monitoring and climate research (de Araújo et al.,
Equual contribution 'Technical University of Munich. School of Computation, Information and Technology, Germany UUniversity of Zurich, Department of Mathematical Modeling and Machine Learning, EcoVision Lab, Switzerland 'Technical University of Munich, School Computation, Information and Technology, Dy- namic Vision and Learning Group. Germany "Technical University of Munich, School of Life Sciences, Department of Life Science Engineering, HEF World Agricultural Systems Center, Chair of Digital Agriculture, Germany. Correspondence to: Malte von Bloh <malte von. wwo..bloheee. bloh@tum.de>>
Proceedings of the 42nd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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EcoMapper: Generative Modeling for Climatc-Aware Satellite Imagery
Spatial Prompt
Example Prompt
Spatial Prompt: a partially cloudy satellite image of Croplands in Nebraska, United
Cloudiness
Clip Text Encoders
States May 2020.
Land Type
Climate Prompt: The average temperature over last month was 25 with an average precipitation of 2 mm and an average daily solar radiation of 25 kWh/m2.
Location
Date
Prompt Embedding
Stable Diffusion 3 Transformer Block Linear Layer
ControINet
Climate Prompt
Stable Diffusion
Temperature
T5 Text
Encoders
Precipitation
Stable Diffusion 3 Trainable Copy
Control Prompt Embedding
Solar Radiation
Encoder
Decoder
(H,
Control Image
Generation Prompt Embedding
Generated Image
Figure 1. Our framework enables multi-conditional (text+image) satellite image generation using Stable Diffusion 3 (Esser et al., 2024) and ControlNet (Zhang al., 2023). On the text-input side, the model takes detailed spatial and climatic prompt embeddings created by CLIP and T5 text encoders. On the image-input side, ControlNet is fine-tuned to process an image for spatial guidance. Both is passed to pre-trained Stablc-Diffusion which generates location- and climate specific satellite image, aligned with actual spatial characteristics.
depend historical approximated satellite data in the absence of integration with climate scenarios. This results in qualitative gap between predicted environmental condi- tions and available imagery, complicating the representation of dynamic changes in land cover or vegetation states, re- ducing the predictive power of these models (Ebel et al., 2020; Jozdani et al., 2022). These challenges underscore the need for synthetic satellite imagery enhance datasets and provide realistic projections for future conditions. In this paper, we introduce a novel approach to generate satellite images conditioned on geographic- -eimmitee prompts using Stable Diffusion 3. Our method enables the simulation of how weather and climate affect Earth's 's surface generating synthetic images that can support forecasting models (e.g., crop yield prediction or land cover classification), visualize climate change models under various scenario assumptions, and fill observational gaps in regions affected by persistent cloud cover. The approach is globally applicable and gener- ates realistic images with 10-meter spatial resolution across diverse vegetation types (e.g., cropland, broadleaf forests, savannas), using information about location, land cover type, and climate conditions. We propose two innovations:
curated dataset of over 2.9 million RGB satellite images from 104,424 locations worldwide, sourced from Sentinel-2 (Drusch et al., 2012). This dataset spans the whole Earth cat- egorized in 15 vegetation zones and eight years of historical data. Together, these contributions advance the application of generative models in remote sensing and offer novel solu- tions for a variety of environmental monitoring challenges.
2. Related Work
2.1. Diffusion Models
Diffusion models are powerful class of generative models, achieving state-of-the-art performance in high-quality image synthesis across diverse domains (Ho et al., 2020; Khanna et al., 2024; Zhang et al., 2023). They operate by iteratively denoising random Gaussian noise through learned reverse- diffusion process, producing realistic samples. These mod- els excel in tasks like image super-resolution, inpainting, and domain adaptation (Manvi et al., 2024; Toker et al., 2024), and have been effectively applied to satellite im- agery synthesis by leveraging control images and textual prompts to maintain spatial structure and stylistic fidelity (Sastry et al., 2024). Foundational work by Sohl-Dickstein et al. (2015) introduced diffusion probabilistic models, later refined by Ho et al. (2020) with simplified denoising ob- jective. Score-based generative modeling (Song et al., 2021) and classifier-free guidance (Dhariwal & Nichol, 2021) have enhanced their adaptability and output quality.
1. A text-to-image generation model that leverages Stable Diffusion with climate prompt engincering.
2. A multi-conditional (text+image) framework utilizing ControlNet, which preserves spatial features and en- ables the generation of time series.
Fig. illustrates the concept. To support this research, we
2

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EcoMapper: Generative Modeling for Climatc-Aware Satellite Imagery
2.2. Satellite Image Generation
casting within single diffusion models, achieving state-of- the-art results by capturing complex spatiotemporal relation- ships. DiffCast from Yu et al. (2024a) outperforms previous works in the Critical Success Index (CSI) which measures the fraction of correctly predicted precipitation events rela- tive to all observed or predicted events - by 15.59 %.
In remote sensing, image generation has also been enhanced by diffusion models, with models like Stable Diffusion (SD) fine-tuned generate satellite images from textual descrip- tions (Liu et al., 2024b). Khanna et al. (2024) enriched in their "DiffusionSat" the SD model's input by incorporating geo-location and sampling time as prompts, enabling the generation of high-quality satellite images tailored to spe- cific geographic and temporal conditions. Their experiments focused generating diverse land cover types, including ur- ban areas, croplands, and forests, demonstrating the model's ability to capture tne-grained details such as building struc- tures and vegetation patterns.
3. Preliminaries
3.1. Diffusion
Diffusion models aim to produce images by reversing stochastic Gaussian noising process, Given input image, noisy imput x, is created by adding Gaussian noise:
Diffusion models have also been widely adopted for image- to-image generation, producing realistic satellite images from guiding inputs such as maps, semantic layouts, and multi-modal data. Espinosa & Crowley (2023) successfully generated satellite images conditioned on historical maps, focusing on urban and rural landscapes to simulate histori- cal and future land use changes. Tang et al. (2024) refined the generation process by integrating both global (e.g., tex- tual descriptions) and local (e.g.. depth maps, segmentation masks) control information, expanding the scope of satellite image synthesis.
x /1 3, -1-1 VB..c. C~N(0, (I)
The parameters f3 denote the noise variance at each timestep t, the higher the the more the added noise. The aim of the diffusion model is to denoise random sample using neural network to predict its noise vectors, effectively learning the mapping 2; + Σt-1- This results in stochastic generative model M that maps from a predefined noise distribution NO,, 7) to generated images.
M(=) = vo 2~ ~~(O0,
(2)
Satellite image datasets range from small task-specific col- lections to large general-purpose sets, with unlabeled remote sensing data often exceeding one million images, while la- beled datasets are typically smaller in size. Reben (Clasen et al., 2024) and Earth View (Velazquez et al., 2025) support general-purpose and self-supervised learning, with Earth- View comprising over 15 terapixels of multi-source imagery. fMoW (Christic et al., 2018) and EuroCrops (Schneider et al., 2023) provide labeled data, targeting functional land use and harmonized crop types. Multimodal and cloud- robust datasets such as SENI2MS-CR-TS (Ebel et al., 2022) and DifICR (Zou et al., 2024) offer paired radar-optical time series for cloud removal. In contrast, MetaEarth (Yu al., 2024b) presents a generative foundation model trained multi-resolution imagery for large-scale image synthesis. In contrast, slimate-integrate datasets have so far been smaller and highly specialized for research applications (Nath et al., 2024; Requena- Meaena et al., 2021).
Latent diffusion models are type of generative model that applies the diffusion process within a lower-dimensional la- tent space, which is obtained by encoding the input images, This reduces computational cost compared to traditional diffusion models. A decoder then reconstructs the image from the latent space. Conditional architectures, including Stable Diffusion 2, guide the denoising process by incor- porating an additional signal C(+)), such as text or images. This enables the generation of meaningful outputs that align with the control signal and the specific task for which the model was trained.
MZ, C(=)) = Eo. 2 ~10,
(3)
3.2. ControlNet
ControlNet (Zhang et al., 2023) enhances diffusion models by integrating explicit spatial control into the generative process. Let F(x) denote neural network block from the original architecture, which has frozen parameters internally (omitted here for brevity). The new control block modifies its output by adding
2.3. Integrating Climate Data
When processing datasets with high-dimensional climate variables like cyclone distribution, cloud cover, and water vapor, diffusion models have proven effective (Liu et al., 2024b). Hatanaka et al. (2023) used cascaded diffusion mod- els generate high-resolution cloud cover images, while Nath et al. (2024) employed multi-stage diffusion frame- works for precipitation and cyclone forecasting, Gao et al. (2023) and Leinonen et al. (2023) unified precipitation now-
y F=)(x)+ (F 01); 0com)) (4)
where Ocopy is trainable copy of the original parameters, and 0 and 0₂ are the parameters of zero modules (c.g. convolution layers initialized to zero). This design ensures that the control block has minimal initial effect on the main block, functioning similarly to skip connection.
3

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EcoMapper: Generative Modeling for Climatc-Aware Satellite Imagery
4. Material and Methods
4.1. The EcoMapper Dataset
We present the EcoMapper dataset that combines Sentinel-2 RGB images with corresponding metadata, assembled using Google Earth Engine, Sentinel Hub and NASA Power (Dr- usch et al., 2012; Milcinski et al., 2019; NASA, 2025; Phan et al., 2020).
January
April
July
October
20000
10000
4.1. 1. SATELLITE IMAGERY
The dataset includes 104,424 unique geographic locations, randomly sampled from 15 distinct land cover classes (Phan al., 2020) excluding water bodies as shown Figure For each location, we selected one monthly observation for two year period based on the least cloudy day, resulting in sequence of 24 images per location. The two years of ob- servation are randomly distributed between 2017 and 2022. The test set consists of 5,500 unique geographic locations, each monitored monthly over a 96-month period from 2017 2024. This ensures sufficient spatial and temporal inde- pendence in the evaluation, enabling robust assessment of the model's generalization across diverse regions and unseen climate conditions. With spatial coverage of ~ 26.21 1 km observation the overall dataset covers ~ 2,704,000 km², accounting for 22.5 % of Earth's 's terrestrial area. An ex- cerpt of the dataset is published in the Github repository, the full dataset available the universities servers. For more dataset details we refer to A.I.
Saveeror
E Foreeer
Gropiselt
Wobey Stumoor
Otherr
"Evergreen Broadleaf Forests
Figure 2. The 104,424 locations were sampled globally across 15 land cover types, providing representative distribution of Earth's land surface. Grasslands and sparsely vegetated regions domi- nate, followed by forested and croplands, with additional categories summarized into "Others" (mixed-, evergreen needle- leaf forest, permanent wetlands, cropland/natural mosaics, urban, closed shrubland and decidiuous needleleaf forest). Each location includes a time series of months (training) or months (test- ing).
4.1.2. CLIMATE DATA
Each sampled location is enriched with metadata, including geographic location (longitude and latitude), observation date (month and year), land cover type, and cloud coverage (in %). We incorporated average monthly temperature, solar radiation, and total precipitation from NASA Power (NASA, 2025), as these factors mainly drive vegetation growth, en- ergy availability and water balance, which in turn influence agricultural conditions, forestry, biodiversity, and land cover (Pielke Sr et al., 2011).
which preserves the spatial structure of an input image while mapping climate-induces variations onto it. By leveraging our dataset, we demonstrate how environmental changes can visually represented by modifying climate metadata in the generation process.
We evaluate generative models for their ability to inte- grate climate metadata into satellite image synthesis:
4.2. Generative Models
Stable Diffusion 3 (SD3) from Esser et al. (2024) A mul- timodal latent diffusion model incorporating CLIP and T5 text encoders for flexible prompt conditioning. We fine- tune SD3 using our dataset to generate realistic satellite imagery conditioned on geographie, climatic, and temporal metadata. key challenge is the representation of continu- ous climate variables, which address through structured prompt engineering. DiffusionSat from Khanna et al. (2024) a specialized foundation model for satellite imagery, ex- tending Stable Diffusion 2 with a dedicated metadata em- bedding layer for numerical conditioning. This architecture encodes key spatial and temporal attributes, including lati-
Our goal is to synthesize satellite imagery conditioned geographic and climate metadata, enabling realistic pro- jections of environmental conditions. To achieve this, we leverage state-of-the-pp generative models for two key tasks: text-to-image generation and nulti-conditional image gen- eration.
For text-to-image generation, we employ generation models that synthesize satellite images based on structured metadata prompts. Additionally, we introduce multi-conditional generation approach using a Contro/Net-eenanceed model,

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EcoMapper: Generative Modeling for Climatc-Aware Satellite Imagery
tude, longitude, timestamp, ground sampling distance, and cloud cover. Unlike generic diffusion models, DiffusionSat is explicitly designed for remote sensing tasks, including super-resolution, inpainting, and temporal prediction.
spatial structure and climate dynamics, we incorporate dual-conditioning mechanism using ControlNet. Control- Net extends Stable Diffusion by introducing trainable neural layers that guide the denoising process using an external control signal. In our approach, two critical conditioning signals are used: Satellite imagery from previous months as a control signal that preserves the spatial structure the generated image, ensuring that landforms, urban layouts, and other geographical features remain intact. This also en- ables the model to incorporate temporal changes over time, reflecting real-world environmental shifts. Climate Prompts: A textual conditioning mechanism that specifies climatic and atmospheric conditions under which the satellite image should be generated. By combining these two conditioning factors, the model is capable of generating realistic satellite images that integrate climate variations while maintaining spatial consistency. This method supports time-series gener- ation, allowing the simulation of landscape evolution under changing climate conditions.
4.2.1. TEXT-TO-IMAGE GENERATION
We compare multiple configurations of Stable Diffusion 3 and DiffusionSat, with and without fine-tuning, to assess their capacity for climate-aware satellite image synthesis. The base SD3 model, leveraging T5 encoder, allows full climate prompt conditioning, The base DiffusionSat model, limited to one text encoder with 77 tokens, was only condi- tioned on month, year, cloudiness, and land cover type due to its predefined metadata embedding structure. To enable DiffusionSat fine-tuning with additional climate metadata, we modified its SatUNet architecture by introducing 10 metadata layers. We initialized this adapted network with pretrained DiffusionSat weights while randomly initializing the new layers, followed by full-model fine-tuning. For a fair comparison, both models were trained at 512x512 res- olution, which aligns with DiffusionSat's original training setup. Additionally, SD3, which supports higher resolu- tions, was tested in a fine-tuned experiment at 1024x1024 resolution. In summary evaluate:
4.3. Prompt Structure
We design two types of prompts to effectively condition satellite image generation: a spatial prompt. which encodes metadata, and a climate prompt extending it with environ- mental details. Both prompts leverage the text encoders of Stable Diffusion 3, with spatial information processed by CLIP and climate data handled by the T5 encoder.
1. Baseline models: Both models were evaluated without fine-tuning at 512x5 12 resolution.
2. Fine-tuned models: Both models were fine-tuned (-FT) with climate metadata at 512x512 resolution.
1. Spatial Prompt:
Captures fundamental metadata, in- cluding land cover type, location, date, and cloudiness. This ensures that the generated images align with the geographic and temporal context.
3. High-resolution SD3: SD3-FT-HR was fine-tuned with
climate metadata at 1024x1024 resolution.
4.2.2. CLIMATE- SENSITIVITY ANALYSIS
2. Climate Prompt: Extends the spatial prompt by incor- porating monthly climate variables temperature, pre- cipitation, and solar radiation providing additional en- vironmental conditioning for image generation.
To assess the sensitivity of the SD3-FT-HR model to cli- mate variables and ensure that performance gains stem from meaningful climate conditioning rather than spurious cor- relations with month, land cover, or location we perform a targeted sensitivity analysis. We evaluate the model's ability to incorporate climate effects into satellite image generation by testing it under extreme conditions, ranging from dry bo- real (cold, dry) to humid tropical (hot, wet) climates. These variations span multiple land cover types and regions, en- abling us to determine whether the model captures genuine climate influences or merely exploits dataset correlations.
The structured prompt follows the format:
"A satellite image of [land cover type] in [location] on [date]. The average temperature was [temperature]. with [precipitation] and [so- lar radiation]." This format ensures the generated images remain contextually and environmentally accurate by inte- grating both spatial and climatic factors. For example: "A satellite image of croplands in Northern Cape, South Africa, on October 2019. The average temperature over the last month was 20%C, with an average precipitation of 0 mm and an average daily solar radiation of 25 Wmm?" This structured prompting framework enables effective condi- tioning across spatial and environmental dimensions. Ad- ditional details the prompting strategy and comparative results for different prompt formulations are provided in Appendix A.3.5.
4.2.3. MULTI-CONDITIONAL IMAGE GENERATION
For the task of multi-condiiional (text+image) image gen- eration, utilize fine-tuned Stable Diffusion 3 model enhanced with LoRA (Low-Rank Adaptation). This model, trained at 512x512 resolution, serves as foundational prior for generating high-quality and contextually relevant outputs. To condition the image generation process on both
5

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EcoMapper:
Generative Modeling for Climatc-Aware Satellite Imagery
4.3.1. METRICS
QT
DiPlusionBat-FT
303-FT
303-FTHR
To evaluate the quality, diversity, and perceptual fidelity of the generated satellite images, we use five established met-
Open Shrualnnds
Australia
rics: FID, LPIPS, SSIM, PSNR, and CLIP Score. FID and LPIPS assess realism and perceptual similarity, while SSIM and PSNR measure structural consistency and reconstruc- tion quality. CLIP Score evaluates text-image alignment. A detailed description is provided in A.2.
New Zealand
5. Results
China
5.1. Texl-to-Image Generation
We test our models on 5,500 worldwide locations across all land cover types, using eight years of satellite data with monthly observations. As shown in Tab. 1, the baseline mod- els from SD3 and DiffusionSat had the lowest evaluation scores. However, DiffusionSat demonstrated superior per- formance over SD3, showing advantages from the satellite- specific pretraining. Fine-tuning significantly improved both models across all metrics: SD3-FT achieved higher CLIP, SSIM, and PSNR scores, while DiffusionSat-F" FT excelled in FID and LPIPS. The best-performing model in terms of FID) was SD3-FT-HR, which produced the highest-resolution images.
Kazakhsstn
Grasollanda
Parauuyy
Canade
Barmen
The qualitative results in Fig. 3 demonstrate the capabil- ity of our models in generating realistic satellite images conditioned on geographic and land type metadata. In par- ticular, the models excel at capturing the structured patterns of croplands/grasslandd. such as those seen in Kazakhstan and Paraguay, where regular field patterns are faithfully reproduced. All three models effectively preserve the essen- tial structure of these landscapes. In mountainous regions like Canada, the models successfully capture the distinct features of snow-covered terrains and rocky surfaces. While both architectures handle snow coverage well, SD3-FT-HR excels at preserving the sharp contrasts between snow and rock formations, providing finer detail compared to Diffu- sionSat in this context. In grassland regions of Paraguay, the models represent the expansive, flat terrain with sparse vegetation effectively, capturing the homogeneous struc- ture typical of grasslands. All models manage to represent the broadness of these regions, though SD3 models show a slight improvement in capturing the subtle variations in vegetation density. For wetland areas DiffusionSat captures the water presence with high fidelity, while SD3 also effec- tively represents the wetland's dynamic structure, with both models excelling in maintaining the spatial features, though with slight differences in texture and detail depending the model configuration. A comparison of both pretrained and fine-tuned models with details about the climate data can be found in Appendix 10.
Parmarant Wattands
Pallsan
Figure 3. Qualitative comparison of satellite images generated by different models. The columns represent Ground Truth (GT), DiffusionSat-FF SD3-FT, and SD3-FT-HR, where FT denotes fine-tuned and HR high-resolution models. Each row specifies the country on the left and the corresponding land cover type on the right.
5.2. Climate-Aware Sensitivity Analysis
The sensitivity experiments in Fig, 4 show notable varia- tion in generated vegetation across different climate condi- tions. In humid environments, the generated images exhibit denser vegetation, whereas in cold and dry conditions, the outputs display barren or snow-covered landscapes. Across most land cover types, there an observable increase sim- ulated vegetation as climate inputs shift towards warmer and more humid conditions. Conversely, drier and colder con- ditions result in sparser vegetation and more exposed land surfaces. However, the magnitude of these effects varies by region. In areas where the prompted climate conditions deviate strongly from the typical ones of the region, the model exhibits weaker response or no observable change.
6

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EcoMapper: Generative Modeling for Climatc-Aware Satellite Imagery
Table Quantitative comparison of text-to-image generation mad- els. The base models refer to the original models without fine- tuning, while the "-FT" models have been fine-tuned at resolution of 512x512 pixels. The SD3-FT-HR model was fine-tuned and trained at higher resolution of x024x 1024 pixels.
Table 2. SD3-FT Performance on extreme weather conditions
Condition
FIDJ
CLIPT
SSIMT
PSNR1
LPIPS,
High Temperature
115.33
0.35
0.47
15.52
0.64
Low Temperature
145.10
0.35
0.26
11.78
0.756
High Precipitation
170.80
0.40
0.36
11.92
0.72
Low Precipitation
85.62
0.33
0.43
15.10
0.66
Model
FID
CLIP
SSIM
PSNR 1
LPIPS !
High Radiation
107.34
0.36
0.46
15.83
0.64
SD3
157.36
0.29
0.14
8.25
0.85
Low Radiation
141.37
0.32
0.25
11.65
0.77
DiffusionSat
115.00
0.31
0.25
10.18
0.80
SD3-FT
77.9
0.33
0.39
14.24
0.70
DiffusionSat-FT
59.8
0.30
0.33
12.14
0,68
SD3-FT-HR
49.48
0.31
0.36
12.01
0.69
> 25 W W/m²). This setup enables focused assessment un- der rare climate scenarios. Results across standard metrics are shown in Table 2.
For instance, when simulating snowy conditions in arid vironments such as the United Arab Emirates, the generated images show only minimal or no adaptation to the extreme climate input.
5.3. Multi-conditional Image Generation
Compared to the text-to-image generation models, multi- conditional generation achieves superior performance across metrics, benefiting from added control signals that en- hance its ability to generate more precise images while preserving both spatial structure and environmental factors. For evaluation in Tab. 3 we use the same test set as in the coxt-to-image generation task. As seen in Fig. 5, the gener- ated images exhibit strong spatial alignment with the ground truth images, maintaining key geographical features while incorporating climate-specific changes. For instance, the shift in color grading from brown to green illustrates reason- able simulations of seasonal transitions, such the onset of spring or the growth of vegetation after rainfall, which can also be inferred from increasing precipitation values in the images. The presence of snow in colder conditions reflects the model's responsiveness to low-temperature cli- mate prompts, accurately simulating seasonal changes that would occur naturally in such environments.
Dry Berezi
Temporate
Hurri
Germany
Cropands
Crophadds
Inda
Mied Maned Fonses
Moreover, we observe noticeable shift in grasslands, transitioning from green to brown temperature and solar radiation values increase. This shows a realistic response to environmental conditions such as drought or extreme heat. In certain land classes, such as barren and open shrub- lands, we not observe significant changes, even with higher temperatures. These land types are relatively uni- form throughout the year, which explains the minimal visual differences under varying climate conditions. The model captures this consistency by generating largely unchanged imagery, accurately reflecting their year-round stability, but also indicating a correlation between location, land cover, and the generated image.
Figure 4. Satellite images generated SD3-FT under extreme climate conditions for different regions. Rows correspond to geo- graphical locations (Germany, India, China, and Canada), while columns represent three climate types: Dry Boreal, Temperate, and Humid. Land types (Croplands, Mixed Forests, Grasslands) are labeled on the right.
To complement the visual analysis, conducted a quanti- tative stress test of the SD3-FT model on samples exhibiting extreme weather conditions. These were defined by thresh- olds: temperature (< -10 or 2 30 CC),, precipitation (< 0.01 mm or 2 10 mm), and radiation (S 5 W/m2 or
Table Metries for Stable Diffusion ControlNet model
Model
FID |
CLIP
SSIM 1
PSNR
LPIPS
SD3 ControlNet
48.20
0.32
0.40
13.63
0.59

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EcoMapper: Generative Modeling for Climatc-Aware Satellite Imagery
High Temperature Low Temperature
Low lumidity
Low Temperature
High lkmicity
us to evaluate both. 5,500 test locations are unique and globally sampled across the full time span from 2017 to 2024, while the training set is limited to data from 2017-
Genorabo Image Condition image
2022. This ensures that test samples from 2023 and 2024 represent unseen combinations of both space and time.
As shown in Table 5, the model maintains consistent perfor- mance across all years, with no substantial degradation on 2023-2024 data.
Ground Trush
Table Robustness analysis for Controlnet
Year
FID
CLIP
SSIMI
PSNR1
LPIPS!.
Dataset %
2017
102. 14
0.36
0.40
13,33
0.61
8.48%
Figure 5. Comparison of Ground Truth, Generated Image, and Condition Image for qulti-conditional image generation under sea-
2018
84.12
0.35
0.41
14.00
0.58
12.64%
2019
89,52
0.36
0.41
13,90
0.58
12.38%
sonal changes. The top row displays condition images, while the labels above correspond to the ground truth images in the bottom row. All examples reflect typical seasonal variations (e.g.. tem- perature and humidity) rather than extreme weather events. Color shifts ge.g., brown to green vegetation) and the presence or absenee of snow illustrate the model's sensitivity climate conditions, demonstrating its ability to generate seasonally consistent satellite imagery.
2020
86.37
0.35
0.40
13.40
0.59
12.50%
2021
83,30
0.33
0.40
13,73
0.58
12.14%
2022
77.47
0.33
0.40
13.27
0.58
14.83%
2023
93,21
0.35
0.43
14.09
0.59
9.78%
2024
73.54
0.32
0.40
13.48
0.59
17.34%
6. Discussion
In this study, we analyze the ability of diffusion models to integrate climate effects into satellite image generation. Through sensitivity experiments, we evaluate whether the models are able to capture meaningful climate-related trans- formations. The results show a consistent increase in simu- lated vegetation under warmer and more humid conditions, while cold and dry climates produce sparser vegetation or snow-covered landscapes. This trend aligns with known environmental patterns, where higher precipitation leads to denser vegetation, whereas arid and frigid conditions result in land cover dominated by barren surfaces or snow accumu- lation (Richardson et al., 2013). These observations indicate that diffusion models can learn the relationship between cli- mate variables and environmental features and incorporate these patterns into satellite image synthesis. The sensitivity of the models varies depending on the land cover type. Ev- ergreen zones, such as tropical rainforests, or urban areas, exhibit lower sensitivity to extreme climate prompts than regions with pronounced seasonal changes. This suggests that certain land cover types and locations inherently ex- hibit less variability in their visual characteristics, making climate-induced transformations less prominent. Extreme climate attack scenarios, such as generating snow-covered rainforests, may not represent realistic environmental condi- tions. We argue that correlations between locations or land cover type and typical climate conditions are learned during joint training. Therefore, snowy rainforest, although both features (snow and rainforest) are individually present in the dataset, represents an out-of-distribut work could further refine prompt masking strategies (c.g. partially omitting location or land cover specific informa-
5.4. Robustness Test
5.4.1. PERFORMANCE ACROSS LAND COVER TYPES
We further analyze the model's consistency across diverse land cover types to assess how surface characteristics in- fluence generative performance. Table 4 presents a sub- set of results, highlighting both frequently occurring and challenging landcover classes. Grasslands and savannas well-represented in the training data achieve stable and high-fidelity generations, whereas visually complex or un- derrepresented classes such wetlands and urban areas yield lower scores. The complete per-class evaluation is provided in Appendix Table 7.
Table SD3_FT performance across selected land cover types.
Dataset % indicates class prevalence in the test set (2017-2024).
Land Cover
FIDE
CLIP?
SSIM
PSNRT
LPIPS]
Dalaset %
Grusdands
121.93
0.32
0.38
14.42
0.71
23.7%
Savannas
116.90
0.35
0.38
13.84
0.71
12.0%
Permanent Wetlands
263.36
0.51
0.39
13.73
0.73
0.8%
Urlun/Buill-Up
284.65
0.70
0.06
11.17
0.81
0.7%
5.4.2. SPATIAL AND TEMPORAL ROBUSTNESS
Robustness in climate-aware satellite image generation can be viewed along two axes: spatial generalization producing realistic outputs at previously unseen locations and temporal generalization handling climate conditions from time peri- ods outside the training range. Our test set design allows
generation. Future
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EcoMapper: Generative Modeling for Climatc-Aware Satellite Imagery
tion) for increasing latent space interpolation capabilities.
(1024x1024) resulted in the best-performing model SD3- FT-HR, demonstrating that more details and finer textures were generated effectively. We demonstrated that multi- conditional generation using ControlNet preserves climate conditioning while introducing spatial guidance, making it suitable for generating synthetic satellite images for specific locations (e.g., areas of interest) under historical or future climate scenarios. This capability particularly valuable for applications such as climate modeling, disaster response, and environmental monitoring, where reliable synthetic im- agery can support long-term scenario analysis.
The ablation studies on land cover types and spatiotempo- ral robustness support the expected behavior of the model and further validate its generalization capabilities, SD3-FT shows strong performance on well-represented land classes like grasslands and savannas, while lower scores are ob- served for visually complex or underrepresented categories such wetlands and urban areas. This pattern aligns with findings from prior work on dataset imbalance in generative modeling (Ghosh et al., 2024; Qin et al., 2023) and remote sensing tasks (Leichtle et al., 2017; Xiao et al., 2024). The model also maintains stable performance across both spa- tially and temporally unseen samples, including new climate conditions from 2023 and 2024. This indicates that EcoMap- per is applicable across varying locations, years, and land cover types- supporting that consistent behavior can be expected in future scenarios.
7. Conclusion
In this study, we introduced the comprehensive EcoMapper models and dataset linking climate data with corresponding Sentinel-2 satellite imagery and land cover types, enabling the generation of glmate-conditione satellite images. Our results demonstrate that diffusion models effectively map climate effects onto remote sensing imagery, with satellite- specific models outperforming general- purpose diffusion models. These findings highlight the potential of diffusion models as a valuable tool for generating past and future climate-condittiond satellite images, supporting applica- tions in agriculture, forestry, climate change analysis and many other domains. Future research could explore higher time-resolution climate inputs to enhance the accuracy of Sumate-Earth surface interactions. Extending this approach to multispectral image generation would increase its ap- plicability to environmental monitoring, where vegetation indices rely both visible and non-visible spectral bands. Linking diffusion-based image generation with climate pro- jection models could enable seasonal climate simulations, providing insights into potential land surface changes un- der different climate scenarios. These advancements would further solidify generative models as a powerful tool for climate-aware Earth observation.
The diffusion model's capability of mapping climate to satellite images is also reported in previous work, such as Leinonen et al. (2023) and Gao et al. (2023), who success- fully integrated weather conditions into satellite imagery, al- though their focus was on nowcasting precipitation. A direct comparison is difficult, to the best of our knowledge, prior work has explicitly studied global climate- conditionn satellite image generation using diffusion models. Com- pared to cloud removal tasks, which typically report FID scores exceeding values of 120, our results suggest that generating coimate-oonditionee satellite images is a more learnable task, which can likewise be used to generate cloud- free images (Liu et al., 2024b). When compared a broader range of ceneral-purpose satellite image synthesis models, our FID scores are in line with models from other domains. In super-resolution tasks, authors have reported FID scores of 32 (Zheng et al., 2024), 29-40 (Wang & Sun, 2025), and about 16-25 from Khanna et al. (2024), Toker et al. (2024) and Yu et al. (2024b) in image generation tasks. Our met- rics slightly exceed these values, suggesting that generating climate-conditioned visual effects on Earth's surface might be moderately complex task, potentially requiring addi- tional line-tuning strategies, larger models or datasets.
8. Code and Data Availability
The code is available under: ma wettee/eccommee The dataset is available under: doi:10.14459. 2025mp176765
Our experiments highlight the importance of pretraining on remote sensing data for climate-aware satellite image gener- ation. Although DiffusionSat was not explicitly trained on climate data, it still outperformed the general-purpont SD3 model, suggesting that prior exposure to satellite imagery provides significant advantage. Fine-tuning on climate metadata improved all models, reinforcing the benefits of incorporating climate information into satellite image gen- eration. However, even after fine-tuning, Diff maintained a slight edge over SD3-FT, likely due its specialized satellite image generation capabilities. Despite this, the ability of SD3 to be trained at higher resolutions
Acknowledgement
We thank the European Space Agency Network of Re- sources and Horsch Maschinen GmbH for support to acquire the satellite imagery dataset.
9

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EcoMapper: Generative Modeling for Climatc-Aware Satellite Imagery
Impact Statement
Dubovik, O., Schuster, G. L.., Xu, F,, Hu, Y., Bösch, H., Landgraf, J., and Li, Z. Grand challenges in satellite remote sensing, 2021.
EcoMapper introduces generative framework for simu- lating satellite imagery conditioned on climate variables, with the goal of modeling how environmental landscapes respond to weather and long-term climate change. This enables new opportunities for climate change impact visu- alization, scenario exploration, and enhancing downstream models that integrate satellite and climate data-such as crop yield forecasting, land use monitoring, or image gap-filllg sap-filling in cloudy regions. We acknowledge that synthetic imagery carries risks of misinterpretation if used without proper con- text uncertainty modeling. This is particularly relevant in high-stakes settings like natural disaster assessment policy-making where unreliable outputs could cause harm. We encourage the responsible use of EcoMapper as a sim- ulation tool to complement rather than replace- physical Earth observation in real-world decision-making
Ebel, P., Meraner, A., Schmitt, M., and Zhu, X. X. Multisen- sor data fusion for cloud removal in global and all-season sentinel-2 imagery. IEEE Transactions Geoscience and Remote Sensing, 59(7):5866-558 2020.
Ebel, P., Xu, Y., Schmitt, M., and Zhu, X. X. Sen 2ms-cr-ts: A senssig data set for multimodal multitemporal cloud removal. IEFE Transactions on Geoscience and Remote Sensing, 60: 60:1-14, 2022.
Espinosa, M. and Crowley, E. J. Generate your own scotland: Satellite image generation conditioned on maps. NeurIPS 2023 Workshop on Diffusion Models, Aug 2023.
Esser, P., Kulal, S., Blattmann, A., Entezari, R., Müller, J., Saini, H., Levi, Y., Lorenz, D., Sauer, A., Boesel, F., Podell, D., Dockhorn, T., English, Z., and Rombach, Scaling rectified flow transformers for nigh-resolution image synthesis. In Proceedings of the s International Conference on Machine Learning. ICML 24. JMLR. 2024.
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EcoMapper: Generative Modeling for Climatc-Aware Satellite Imagery
A. Appendix
A.1. Data
The Ecomapper dataset consists of over 2.9 million satellite images with climate metadata. includes RGB imagery and corresponding metadata from the Sentinel satellite missions, covering various land cover types and temporal data points. The training contains 98,930 locations, each spanning 24 months of data, while the test set includes 5,494 locations, each covering 96 months. Tab. 6 has details about the structure.
Table Yearly data for batches from 2017 to 2022, showing number locations each batch across three time groups. Each time group consists of three batches, with two large batches of 14,000 locations and one smaller batch of 5,000 locations, totaling 33,000 locations per group. Each batch sampled randomly according to the earth's 's distribution of landcover types. The test batch, with 5,500 locations, is included for all eight years, while the other batches cover two years each. The total image count across all batches is 2,904,000. Note: There are some missing locations due to the fitting of the earth's 's land cover distribution.
Batch/Year
2017-2018
2019-2020
2021-2022
2023-2024
Total Image
Batch-1, 2,
33,000
0
0
0
792,000
Batch-4, 5,
0
33,000
0
0
792,000
Batch-7, 8,
33,000
792,000
Test Batch
5,500
5,500
5,500
5,500
528,000
Total Image
924,000
924,000
924,000
132,000
2,904,000
For each temporal data point, the metadata also provides weather data, including temperature, solar radiation, and precipita- tion. The satellite imagery originates from the Copernicus Sentinel-2 mission, supported by the European Space Agency (ESA) NoR program. The climate data, consist of average monthly temperature, solar radiation, and total precipitation, gathered from NASA Power Api (NASA, 2025).
25000
24%
20000
Count
15000
12%
10%
10000
9%
9%
9%
5000
4%
2%
2%
1%
0.7%
0.4%
0.2%
Barren or Sparsely Vegetated
Grasslands
Savannas
Evergreen Broadleaf Forests
Open Shrublands
Croplands
Woody Savannas
Deciduous Broadleaf Forests
Mixed Forests
Evergreen Needleleat Forests
Croptand/NNatural Vegetation Mosaies
Permanent Wetlands
Urban and Built-Up Up
Deciduous Needleleaf Forests
Closed Shrublands
Land Cover Types
Figure distribution land cover types, showing count category. The dataset includes distinct land cover types.
sampled using Google Earth Engine land cover maps from Phan al. (2020).
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EcoMapper: Generative Modeling for Climate-Aware Satellite Imagery
Woody Savannas
Urban and Built-Up
20
Savannas
Pemmaeet Wetlands
Open Shrublands
-15
Land Cover Type
Mixed Forests
Grasslands
Evergreen Needleleaf
-10
Evergreen Broadleaf Forests
Deciduous Needleleaf Forests
Deciduous Broadleaf Forests
Croplands
Cropland/Natural Vegetation Mosaics
Closed Shrublands
Barren or Sparsely Vegetated
10
15
20
25
Average Temperature ("C)
Figure 7. Average lemperature for each land cover type. The temperature data was gathered from the NASA Power Api, representing monthly average temperatures the sampled locations.
Woody Savannas
Urban Built-Up
Savannas
Permanent Wetlands
Open Shrublands
Land Cover Type
Mixed Forests
Basion (rmm))
Grasslands
Evergreen Needleleaf Forests
Evergreen Broadleaf Forests
Deciduous Needleleaf Forests
Deciduous Broadleaf Forests
Croplands
Cropland/Natural Vegetation Mosaics
Closed Shrublands
Barren or Sparsely Vegetated
Average Precipitation (mm)
Figure Average precipitation for each land cover type. The precipitation was gathered the NASA Power Api. representing monthly average precipitation across the sampled locations.
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EcoMapper: Generative Modeling for Climatc-Aware Satellite Imagery
25000
Number of Locations
20000
15000
10000
5000
0
Asia
Africa
Europe
North America
South America
Seven seas (open ocean)
Oceania
Antarctica
Continents
Figure 9. The distribution of locations across continents, illustrating the total count of locations within cach continent. Water bodics and Antarctica were excluded from the sampling area.
A.2. Evaluation Metrics
To comprehensively assess the quality of the generated satellite images, we employ the following evaluation metries:
1. Learned Perceptual Image Patch Similarity (LPIPS) (Zhang et al., 2018): Measures perceptual similarity between images using deep feature embeddings. Lower values indicate higher similarity, aligning closely with human perception.
2. Fréchet Inception Distance (FID) (Heusel al., 2017): Evaluates realism by comparing feature distributions real and synthetic images using deep network embeddings. Lower values indicate more realistic images.
3. Structural Similarity Index (SSIM) (Wang al., 2004): Assesses structural similarity between images based luminance, contrast, and texture. Higher values (closer to 1) indicate greater similarity.
4. CLIP Score (Radford et al., 2021): Evaluates how well generated images align with textual descriptions using CLIP embeddings. Higher values indicate stronger alignment with the provided text prompt.
5. Peak Signal-to-Nore Ratio (PSNR) (Wang et al., 2004): Measures pixel-wise reconstruction accuracy using mean squared error. Higher values indicate lower distortion and better image quality.
A.3. Experiments
In this section, outline the experimental setup for fine-tuning three distinct models: DiffusionSat, SD3, and an enhanced variant of DiffusionSat incorporating additional climate metadata. Each model was fine-tuned to generate climate-awan satellite imagery, conditioned on both geospatial and climate data. By leveraging these models, we aim to produce more accurate and contextually relevant satellite images, integrating climate-specific information to improve the representation of dynamic environmental conditions.
A.3.1. FINE-TUNING DIFFUSIONSAT (TEXT-TO- IMAGE GENERATION)
For the DiffusionSat experiments, started with the pre-trained checkpoint of the DiffusionSat.555 512 model, which had been trained for 150,000 iterations. This checkpoint was originally fine-tuned on dataset of 512 images. To enhance the model's s capability to capture climate-related information, we added 3 additional metadata features temperature, solar radiation, and precipitation bringing the total number of metadata features to 10. These additional layers were incorporated into the SetUNet architecture, which serves as the backbone of DiffusionSat.
The architecture was initialized with 10 metadata layers, with the weights from the pre-trained DiffusionSat 512 checkpoint
16

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EcoMapper: Generative Modeling for Climatc-Aware Satellite Imagery
SD3
DifusionSat
DiflusinSal.FT
SDS-FT
9D3-FT-HR
Close shrublends
tempereture 27
Weslam Australia
peropitaion 0
January 2002
solar raciation: Se
Evergreen Nocdielbal Forests
Temperature:
New Zealend,
procipration:
December 2020
solar mdiction: 12
Barren or Spereely Vegetated
tempersture:
Xinjang, China
precipitation
November 2022
radiction:
Croplands
Iemparature
Akmila Region, Kazashsttan
precipiation:
May
solar mdiation: 21
Grasslands
temporature:
Pareguay
precipitation
October 2018
solar radiation: 16
Figure 10. Comparison of generated satellite images from various models, including base models and fine-tuned models. The columns show images from the following models: Ground Truth (GT), SD3. DiffusionSat. DiffusionSat-FFT, SD3-FT, and SD3-FT-HIR, where FT denotes fine-tuned and HR high-resolution models. Each row represents a different geographical region, with associated metadata provided for Ground Truth images, including temperature, precipitation, and solar radiation. The rows correspond to the following regions: (1) Close Shrublands in Western Australia (January 2022). (2) Evergreen Needleleaf Forests in New Zealand (December 2020), (3) Barren or Sparsely Vegetated Xinjiang. China (November 2022). (4) Croplands in Akmola Region, Kazakhstan (May 2017), and (5) Grasslands in San Pedro, Paraguay (October 2018). The metadata the right of each image includes the temperature ("C), precipitation (mm), and solar radiation (Wm?)) for each location. This figure highlights the performance improvements achieved by fine-tuned models compared their base counterparts.
loaded into the model. The three newly added metadata layers were initialized randomly following a normal distribution (0, 1), while the rest of the layers retained their original weights from the pre-trained model. This allowed the model to retain the general learning from the pre-trained checkpoint while adapting to the new climate data.
We fine-tuned this modified all layers of the model i.e. 900 million parameters approximately million satellite images, for 2 epochs. During fine-tuning, the encoder, decoder, and the CLIP text encoder were kept fixed, and only the new metadata layers were updated.
training set, which consists of
A.3.2. FINE-TUNING SD3 (TEXT-TO-IMAGE GENERATION)
For the SD3 model, we fine-tuned two versions: SD3-FT and SD3-FT-HR.
For the fine-tune tie-ttming of the SD3-FT model, we applied LoRA (Low-Rank Adaptation) (Hu et al., 2022) by adding adapter layers the top of the Transformer block, which introduced approximately 18 million trainable parameters. LoRA effectively reduces the number of trainable parameters while maintaining high model performance. Despite this reduction. we observed blurry output images after fine-tuning. To address this issue, we fine-tuned the last two layers of the model the projection layer and the normalization layer since these layers are crucial for output quality, particularly in the final stages of the image generation process. The projection layer is responsible for mapping internal representations to the output space, while the normalization layer ensures stable and high-qualit output distributions. Fine-tuning these layers helped improve the sharpness of the generated images. The model was trained for epochs on our training set.
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EcoMapper: Generative Modeling for Climatc-Aware Satellite Imagery
In addition, we implemented captioning strategy during training introduce variability in the input captions. This strategy aimed reduce the direct correlation between location, month, and climate data in the model's learning process. Typically, satellite images exhibit strong correlation between temporal information (like month and year) and environmental factors (like temperature). For example, January in Russia is usually cold, and this information can be easily inferred from the month metadata. However, we wanted the model to learn to generate images based on climate data (such as temperature, precipitation, and solar radiation) independently of the month and location, allowing it to focus more on the environmental conditioning provided by the climate prompt.
Specifically, for each caption in the dataset, we randomly altered the inclusion of certain metadata components. With 50% chance, we either removed the month or the location from the caption. More specifically, for the month and year:
25% of the time, we removed both the month and the year.
50% of the time, we included both the month and the year.
25% of the time, we included only the year.
For the location metadata:
25% of the time, we removed both the state and country.
50% of the time, we included both the state and country.
25% of the time, we included only the country.
This randomization was applied every time the caption was generated during training, ensuring diverse set of training inputs and helping the model generalize better to varying levels of spatial and temporal metadata availability.
This approach was validated in the extreme case experiment 4, where showed that by removing the month from the caption and changing only the climate data, the generated images exhibited significant differences. This experiment demonstrated that the model could still generate distinct images based on climate variables, even when the month metadata was removed, reinforcing the idea that the model was learning to use climate data effectively rather than relying solely on temporal cues.
For SD3-FT-HR, we used fixed caption i.c with all the spatial and climate data, and we did not use LoRA, opting instead to fine-tune 70 % of the Transformer blocks. Specifically, fine-tuned 1.5 billion parameters, which made training computationally expensive. Additionally, we reshaped the training images from 512 to 1024 resolution, as recommended in the original SD3 paper for achieving the highest-quality outputs. Fine-tuning was performed on 60 % of our training set, which corresponds to 1.2 million images. Training on 1.5 billion parameters resulted in a much slower training process, taking 5-6 times longer compared to the SD3-FT model with LoRA. Despite the longer training time, this approach allowed the model to benefit from higher-resolutee images, critical for capturing finer details in satellite imagery.
A.3.3. FINE-TUNING SD3 CONTROLNET
For the SD3 ControlNet model, chose SD3-FT as the prior due to its faster training time and lower computational cost, which was particularly important given our limited resources. The SD3-FT model was fine-tuned using 512x512 resolution images, striking balance between performance and computational efficiency. This model was trained with LoRA (Low-Rank Adaptation) to reduce the number of trainable parameters while preserving high performance. The key advantage of SD3-FT is its efficient use of resources, as it requires fewer parameters compared to higher-resolution models, such as SD3-FT-HR, making a more practical choice for our task.
To fine-tune the SD3 ControlNet model, we transferred the weights from the first 12 layers of the SD3-FT model and the LoRA adapter into ControlNet. We then fine-tuned all layers of Non,rolNet, including the LoRA adapter, while freezing the transformer layers. This resulted in fine-tuning 1.1 billion trainable parameters. The reason for fine-tuning not only the LoRA weights but also the ControlNet layers is that fine-tuning just the LoRA layers would not have been sufficient to preserve the spatial structures in the generated images, as the LoRA adapter alone has only 18 million parameters. By fine-tuning additional ControlNet layers, we ensure that the model captures the spatial information necessary for generating accurate and high-quality satellite imagery, especially when incorporating Hiimtee-specfff changes.
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EcoMapper: Generative Modeling for Climatc-Aware Satellite Imagery
We trained the model for 2 epochs on dataset of approximately 2 million images, which allowed the model to effectively learn climate-aware image generation within computational constraints. Although SD3-FI-HR has been shown produce higher-quality images, we opted for SD3-FT to optimize training speed and resource usage, the 512x512 resolution model offered an effective trade-off between image quality and computational efficiency. The results from fine-tuning SD3-FT demonstrated good performance in generating spatially accurate, climate-aware images, validating this approach.
A.3.4. ABLATION PERFORMANCE ACROSS LAND COVER TYPES
We conduct ablation study to evaluate the robustness of SD-FT and ControlNet's generation performance across various land cover types. Specifically, assess how well the model generalizes when conditioned different control maps representing diverse land cover categories.
To this end, we compute performance metrics on the full 2017-2024 test set and report averages per land cover class. As shown Table 7 and 8, generation quality tends to correlate with the prevalence of each class in the training data. Frequent classes such as Grasslands and Savannas yield more stable results, while tess-represented classes like Wetlands and Urban/Built- Up exhibit degraded performance. This suggests that data distribution plays a significant role in generation robustness and fidelity.
Table 7. SD3 performance across land cover types. Dataset indicates class prevalence in the test set (2017-2024).
Land Cover
FID
CLIP
SSIMT
PSNRT
LPIPS
Dataset %
Grasslands
121.93
0.326
0.381
14.42
0.715
23.7%
Barren/Sparse Vegetation
103.57
0.355
0.514
15.62
0.572
16.1%
Savannas
116.9
0.35
0.38
13.84
0.71
12%
Open Shrublands
136.08
0.337
0.362
14.64
0.754
9.9%
Evergreen Broadleaf Forests
164.17
0.343
0.425
12.97
0.724
9.9%
Woody Savannas
136.65
0.353
0.334
13.16
0.713
9.1%
Croplands
181.38
0.336
0.383
14.32
0.725
9.0%
Mixed Forests
200.36
0.357
0.309
14.06
0.751
3.6%
Dec. Broadleaf Forests
250.01
0.402
0.369
14.76
0.759
2.1%
Evergreen Needleleaf Forests
235.00
0.441
0.291
13.14
0.757
2.0%
Crop/Nat. Veg. Mosaics
252.47
0.377
0.316
13.89
0.726
1.1%
Permanent Wetlands
263.36
0.516
0.395
13.73
0.739
0.8%
Urban/Built-Up
284.65
0.701
0.064
11.17
0.811
0.7%
Closed Shrublands
340.73
1.489
0.402
16.02
0.779
0.4%
Dec. Needleleaf Forests
366.61
1.874
0.256
13.36
0.759
0.2%
These results emphasize the importance of class distribution balance in training datasets for generative models. Future work may explore targeted augmentation or class- class-aware sampling strategies to improve performance on underrepresented land cover types.
A.3.5. ABLATION PROMPTING STRATEGIES FOR CLIMATE CONDITIONING
We evaluated multiple prompting strategies to determine their effect on generation performance when conditioning the model on climate information. In all configurations, the CLIP encoder input was kept constant using short spatial prompt to maintain consistent image-text alignment. Variations were introduced only in the T5 encoder input, which incorporated climate-related information.
The following strategies were compared:
Numerical Climate Data Short Prompt: Combined a spatial prompt (e.g., region and time) with continuous
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EcoMapper: Generative Modeling for Climate-Aware Satellite Imagery
Table ControlNet performance across land types. Dataset indicates class prevalence in the test (2017-2024).
Land Cover
FID4
CLIP
SSIM
PSNR1
LPIPS
Dataset %
Grasslands
65.27
0.33
0.40
14.06
0.60
23.7%
Barren/Sparse Vegetation
67.44
0.32
0.60
20.18
0.40
16.1%
Savannas
118.73
0.33
0.37
10.73
0.65
12%
Open Shrublands
88.25
0.34
0.41
15.95
0.58
9.9%
Evergreen Broadleaf Forests
122.37
0.37
0.39
9.13
0.68
9.9%
Woody Savannas
109.99
0.33
0.31
10.32
0.64
9.1%
Croplands
100.48
0.34
0.29
12.91
0.60
9.0%
Mixed Forests
148.46
0.36
0.29
11.11
0.65
3.6%
Dec. Broadleaf Forests
172.39
0.42
0.33
11.81
0.69
2.1%
Evg. Needleleaf Forests
169.07
0.44
0.27
10.88
0.68
2.0%
Crop/Nat. Veg. Mosaics
214.63
0.42
0.35
12.16
0.62
1.1%
Wetlands
239.32
0.50
0.38
8.61
0.67
0.8%
Urban/Built-Up
214.36
0.66
0.29
12.01
0.55
0.7%
Closed Shrublands
215.98
0.47
0.18
8.28
0.62
0.4%
Dec. Needleleaf Forests
297.67
0.52
0.28
12.17
0.65
0.2%
climate variables such as temperature, precipitation, and solar radiation. This configuration yielded the best overall performance.
Categorical Climate Data Short Prompt: Climate variables were discretized into interpretable labels (e.g.. "hot," "moderate," "extreme precipitation") and embedded in the prompt alongside the spatial component.
Numerical Climate Data Short Prompt with Dropout: Introduced stochasticity by randomly omitting segments of spatial and temporal metadata during training, simulating missing uncertain input conditions.
Categorical Climate Data Only: Used only categorical climate descriptors without the spatial prompt, requiring the model to rely entirely on environmental cues.
Table reports results for the first three strategies under comparable conditions. The fourth strategy was omitted from benchmarking due lower stability and limited relevance to our baseline.
Table 9. Evaluation of prompting strategies.
Strategy
FID4
1St
CLIP
LPIPS
PSNRT
SSIM
1. Numerical + Short Prompt
68
4.7
0.33
0.66
13.11
0.35
2. Categorical + Short Prompt
72
4.8
0.33
0.69
11.90
0.35
3. Numerical Dropout Short Prompt
94
0.35
0.71
13.30
0.35
These results highlight the advantage of preserving continuous climate signals in combination with spatial context. The dropout-based prompt introduces useful variability but the cost of higher perceptual dissimilarity. A broader investigation of prompt design, including hybrid and dynamically adapted strategies, remains promising direction for future work.
A.3.6. RELATION TO SATCLIP
We acknowledge the relevance SatCLIP (Klemmer et al., 2024) to our work and include a brief discussion here. SatCLIP a contrastive learning framework for satellite imagery that aligns multi-spectral Sentinel-2 images with textual descriptions.
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EcoMapper: Generative Modeling for Climatc-Aware Satellite Imagery
While its objectives related of CLIP-like models for image-text alignment, its architectural design and input requirements differ significantly.
Specifically, SatCLIP operates on 13-band Sentinel-2 imagery, whereas our dataset contains only RGB images. Adapting RGB data to match SatCLIP's input format would require artificial padding of missing channels, which can introduce noise and compromise the integrity of learned representations. To assess compatibility, we conducted pilot evaluation using SatCLIP embeddings on our RGB-based generations and ground truth imagery.
Table SatCLIP similarity scores for different models evaluated on RGB imagery. Lower (negative) scores indicate weaker alignment under SatCLIP's multi-spectie embedding space.
Model
Avg. SatCLIP Score
SD3 FT HR
-0.0148
SD3_FT
-0.0105
Diffsat FT
-0.0285
Ground Truth
-0.0171
The results confirm a significant mismatch: even ground truth RGB images yield negative SaICLIP scores. This outcome reflects the model'ss strong dependence on spectral features unavailable in our dataset, making it unsuitable as a reliable evaluation metric for RGB-only generative models. For this reason, we did not include SatCLIP as core benchmark in our main analysis, though we appreciate the reviewer's suggestion and include it here for completeness.
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Reviews:
[{"review_id": "k9p8gW8fbl", "reviewer": "Reviewer_WAR9", "summary": "The paper introduces EcoMapper, a generative modeling framework designed to synthesize climate-aware satellite imagery. It provides two primary contributions:\n- EcoMapper Dataset: A comprehensive dataset comprising 2.7 million Sentinel-2 RGB satellite images from 104,424 global locations, annotated with climate metadata (temperature, precipitation, solar radiation) and spanning 15 land cover types.\n- Generative Modeling Approaches: \n* Text-to-Image Generation: Uses fine-tuned Stable Diffusion 3 models conditioned on structured textual prompts (geographic location, date, and climate metadata) to generate realistic synthetic satellite images.\n* Conditional Image Generation: Employs ControlNet, enabling guided generation of satellite, allowing realistic representation of climate-driven landscape evolution and seasonal variations.\n\nContributions include:\n- Demonstrating the feasibility of generating realistic satellite imagery conditioned explicitly on climate information, validated through quantitative metrics (FID, CLIP, SSIM, PSNR, LPIPS) and qualitative examples.\n- A sensitivity analysis illustrating the generative models' capability to reflect climate-induced visual changes across various land cover types and climate conditions.\n- Showing improved generative performance through fine-tuning models with climate-specific metadata, especially highlighting the advantage of spatial conditioning via ControlNet for preserving geographical consistency.\n\n## Update after rebuttal\nThank you for clearly addressing my main concerns. The authors have provided important clarifications that strengthen the paper:\n\n- They've revised the introduction to better articulate the intended applications of their approach, including forecasting models, climate change visualization, and filling observational gaps.\n- They've conducted a new temporal validation experiment using 2023-2024 data (beyond their original 2017-2022 training period), demonstrating good generalization across time periods, which is critical for climate-related applications.\n\nThese responses address my primary concerns about application clarity and temporal generalization. The EcoMapper dataset and framework represent a valuable contribution to climate-aware satellite imagery generation. Given these clarifications, I updated my recommendation to accept this paper.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for their constructive feedback and thoughtful evaluation of our work. Below we respond to the key points raised.\n\n**Reviewer comment:**  \nThe paper lacks explicit evidence that the synthetic images are directly useful for downstream tasks like environmental monitoring or scenario planning.\n\n**Response:**  \nWe acknowledge this important point. Our current work focuses on demonstrating the feasibility of climate-aware generative modeling for satellite imagery — a foundational step for downstream applications such as forecasting, scenario simulation, or visualization. Operational deployment (e.g., crop yield prediction, land use modeling) requires additional task-specific pipelines, benchmark data sets and a full set of research experiments, which are beyond the current scope. We note that related work such as DiffusionSat similarly does also not evaluate downstream utility and probably would be an own full paper. However, our method extends prior work by enabling explicit control over continuous climate variables, making it well-suited for simulation-based use cases. Prior studies (e.g., Liu et al., 2024; Toker et al., 2024) have shown that synthetic remote sensing imagery can support applications such as cloud-gap filling, semantic segmentation, and training data augmentation. Strong performance across quantitative metrics (FID, SSIM, CLIP, SATCLIP) signals practical utility. Moreover, synthetic imagery becomes especially valuable where real observations do not exist — such as forecasting future landscapes under climate change. We consider this work a foundational step and are actively exploring downstream integrations in follow-up projects.\n\n**Reviewer comment:**  \n\nThe paper’s intended application (visualization, communication, public awareness) is not clearly stated in the introduction.\n\n**Response:**  \n\nThank you for this helpful suggestion. We revised the end of the introduction to clarify the core use cases of our approach. The updated text reads:\n\n> *“In this paper, we introduce a novel approach to generate satellite images conditioned on geographic-climate prompts using Stable Diffusion 3. Our method enables the simulation of how weather and climate affect Earth’s surface — generating synthetic images that can support forecasting models (e.g., crop yield prediction or land cover classification), visualize climate change models under various scenario assumptions, or fill observational gaps in regions affected by persistent cloud cover. The approach is globally applicable and generates realistic images with 10-meter spatial resolution across diverse vegetation types (e.g., cropland, broadleaf forests, savannas), using information about location, land cover type, and climate conditions.”*\n\n**Reviewer comment:**  \n\nThe dataset split does not consider time, limiting confidence in generalization to future conditions.\n\n**Response:**  \n\nWe agree that temporal independence is critical for evaluating generalization. To address this, we added a new experiment in which all models were evaluated on satellite imagery from **2023 and 2024** at the 5,500 test locations. These years were not included in the former data set (2017–2022), ensuring a strict temporal split. Results show that the model generalizes well across years (see table below). The performance in the new test data does not significant deviate from the other test settings. We will integrate this experiment into the final version of the paper, and release the test data as part of the dataset upon acceptance. Additionally, our globally sampled dataset reduces the risk of encountering entirely unseen climate scenarios, and our edge-case tests (see Fig. 5) further support the model’s robustness to outlier conditions (please see here reviewer vwzA.\n\n**ControlNet Year Metrics**\n\n| Year | CLIP ↑ | SSIM ↑ | PSNR ↑ | LPIPS ↓ | FID ↓ | Dataset share (%)|\n|-|-|-|-|-|-|-|\n| 2017 | 0.36   | 0.40   | 13.33  | 0.61   | 102.14 | 8.48%      |\n| 2018 | 0.35   | 0.41   | 14.00  | 0.58   | 84.12  | 12.64%     |\n| 2019 | 0.36   | 0.41   | 13.90  | 0.58   | 89.52  | 12.38%     |\n| 2020 | 0.35   | 0.40   | 13.40  | 0.59   | 86.37  | 12.50%     |\n| 2021 | 0.33   | 0.40   | 13.73  | 0.58   | 83.30  | 12.14%     |\n| 2022 | 0.33   | 0.40   | 13.27  | 0.58   | 77.47  | 14.83%     |\n| 2023 | 0.35   | 0.43   | 14.09  | 0.59   | 93.21  | 9.78%      |\n| 2024 | 0.32   | 0.40   | 13.48  | 0.59   | 73.54  | 17.34%     |\n\n**Reviewer comment:**  \n\nNo supplementary material provided.\n**Response:**  \nWe would like to clarify that supplementary material begins on page 12 of the main submission. It includes additional information about the dataset structure, evaluation metrics, fine-tuning procedures, and our prompting strategy. We hope this addresses the concern.", "claims_and_evidence": "The paper's main claims—that generative models can produce realistic climate-conditioned satellite imagery—are supported by clear qualitative and quantitative evidence. However, the claim that these synthetic images are directly useful for environmental monitoring, scenario planning, or policy-making lacks explicit evidence from real-world downstream tasks. The evaluation primarily focuses on visual realism rather than practical accuracy or usefulness, making this latter set of claims less convincingly supported.", "methods_and_evaluation_criteria": "The methods used (Stable Diffusion 3 and ControlNet for satellite image generation) and the dataset linking Sentinel-2 images with climate data are appropriate for generating realistic climate-conditioned images. The evaluation metrics (FID, CLIP, SSIM, PSNR, LPIPS) are suitable for measuring visual quality and realism. However, these metrics don't directly measure practical usefulness or accuracy for real-world applications like forecasting, mapping, scenario planning or environmental monitoring. Including additional tests or user evaluations that show practical utility would make the methods more convincing.", "theoretical_claims": "The paper does not include any theoretical proofs or formal mathematical claims.", "experimental_designs_or_analyses": "Yes, I checked the experimental design for evaluating generated images. The experiments clearly compared different generative models using standard visual metrics, which makes sense for assessing image realism. However, the dataset uses random splits without considering time, making it unclear if the model can actually generalize to future climate conditions. This might overestimate how well the model truly performs in real-world extreme climate scenarios.", "relation_to_broader_scientific_literature": "The paper builds on recent advancements in generative models (like Stable Diffusion) and applies them specifically to remote sensing data. Prior works have used similar models for satellite imagery tasks such as super-resolution, image-to-image translation, and cloud removal. This paper uniquely extends these ideas by conditioning the generative models explicitly on climate variables (temperature, precipitation, solar radiation), enabling visualization of climate-driven environmental changes. It also introduces the EcoMapper dataset, significantly expanding available satellite imagery datasets linked to climate metadata.", "essential_references_not_discussed": "None.", "other_strengths_and_weaknesses": "Strengths:\n- The paper combines satellite imagery and climate data using generative AI, which is original and timely.\n- The new large-scale dataset (EcoMapper) is valuable for researchers working on related problems.\n- The examples provided clearly illustrate the model’s ability to represent climate-driven changes visually, making it useful for education, communication, or scenario planning.\n\nWeaknesses:\n- The intended application (visualization, communication, public awareness) wasn't clearly stated upfront, causing some confusion.\n- The evaluation doesn't clearly demonstrate real-world usefulness or accuracy beyond visual quality.\n- The data splitting method doesn't explicitly consider time, limiting confidence in the results for forecasting/scenario planning scenarios.", "other_comments_or_suggestions": "None", "questions_for_authors": "None", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "aECkYMcIL2", "reviewer": "Reviewer_54H1", "summary": "This paper introduces EcoMapper, which combines climate data with satellite imagery based on Sentinel-2 images. Satellite images often face observational challenges, such as areas affected by cloud cover and inherent resolution limitations that can hinder accurate analysis. To overcome these issues, the authors propose a text-prompt-based generative model that leverages both spatial and climate information to synthesize realistic satellite images. This approach promises to enhance the utility of satellite data in applications like environmental monitoring, climate change prediction, and agricultural planning.", "questions": "", "limitations": "", "rating": 1, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the feedback. We would like to address the main critics:\n\n**Reviewer:**  \nThe paper does not provide detailed ablation studies on the selection and impact of different control images.\n\n**Response:**  \nWhile it's not fully clear what is meant by \"selection and impact\" of control images, we interpret this as a question of robustness. To address this, we conducted further analysis of the ControlNet model's performance across land cover types (2017–2024 test set). Results show that classes with higher representation tend to generalize better, while less frequent classes are more challenging to generate consistently.\n\n**Controlnet landcover types**\n|Land Cover|CLIP↑|SSIM↑|PSNR↑|LPIPS↓|FID↓|Dataset %|\n|-|-|-|-|-|-|-|\n|Grasslands|0.33|0.40|14.06|0.60|65.27|23.7%|\n|Savannas|0.33|0.34|10.55|0.65|94.00|21.1%|\n|Barren/Sparse Veg.|0.32|0.60|20.18|0.40|67.44|16.1%|\n|Open Shrublands|0.34|0.41|15.95|0.58|88.25|9.9%|\n|Evg. Broadleaf Forests|0.37|0.39|9.13|0.68|122.37|9.9%|\n|Woody Savannas|0.33|0.36|10.32|0.64|109.39|9.1%|\n|Croplands|0.34|0.29|12.91|0.60|100.48|9.0%|\n|Mixed Forests|0.36|0.29|11.11|0.65|148.46|3.6%|\n|Dec. Broadleaf Forests|0.42|0.33|11.81|0.69|172.39|2.1%|\n|Evg. Needleleaf Forests|0.44|0.27|10.88|0.68|169.07|2.0%|\n|Crop/Nat. Veg. Mosaics|0.42|0.35|12.16|0.62|214.63|1.1%|\n|Wetlands|0.50|0.38|8.61|0.67|239.32|0.8%|\n|Urban/Built-Up|0.66|0.29|12.01|0.55|214.36|0.7%|\n|Closed Shrublands|0.47|0.18|8.28|0.62|215.98|0.4%|\n|Dec. Needleleaf Forests|0.52|0.28|12.17|0.65|297.67|0.2%|\n\n\n**Reviewer:**  \n*It would be valuable to include assessments that measure how closely the generated images match real-world conditions.*\n\n**Response:**  \n\nWe use established metrics such as FID, SSIM, and LPIPS, which are widely applied in both general and remote sensing image generation tasks — including in DiffusionSat (Khanna et al., 2023), which serves as a foundation for our work. This helps ensure that both visual quality and geographic realism are captured. We acknowledge that downstream evaluation  would be a valuable addition. We want to highlight a more detailed answer at reviewer WAR9.\n\n**Reviewer:**  \n\nNo supplementary material was provided\n\n**Response**\n\nWe would like to clarify that we included supplementary material starting on page 12 of the main submission.\n\n**Reviewer:**  \n\nText prompt methodology: The paper lacks sufficient discussion and comparison of different prompting strategies\n\n**Response:**  \n\nWe appreciate this valid point and extend our appendix to include details on the development and evaluation of our prompting strategies. Across all experiments, the CLIP encoder input was kept constant using a short spatial prompt to ensure consistent image-text alignment. Variations were introduced only in the T5 encoder input, which handled climate-related information. We evaluated the following strategies:\n\n- **Numerical Climate Data + Short Prompt** (**best performance**)  \n  Combined a short spatial prompt with continuous climate variables (e.g., temperature, precipitation, solar radiation).\n\n- **Categorical Climate Data + Short Prompt**  \n  Climate variables were discretized in the prompt into human-readable categories (e.g., “hot,” “moderate,” “extreme precipitation”).\n\n- **Numerical Climate Data + Short Prompt with dropout**  \n  Introduced stochasticity by randomly dropping parts of the spatial and temporal metadata from the prompt.\n\n- **Categorical Climate Data Only**  \n  Used only categorical climate descriptors without the spatial prompt, encouraging the model to rely entirely on environmental signals.\n\nDue to computational constraints, we leave a broader investigation of prompting strategies to future work and consider our results here as an initial guide for further exploration.\nBelow we report results for first three prompting strategies under comparable conditions.\n\n| FID | Inception Score | CLIP | LPIPS | PSNR | SSIM | Prompting # |\n|-:|-:|-:|-:|-:|-:|-|\n|68|4.7|0.33|0.66|13.11|0.35|1|\n|72|4.8|0.33|0.69|11.90|0.35|2|\n|94|4.5|0.35|0.71|13.30|0.35|3|\n\n**Reviewer comment:**  \n\nThe number of experiments seems insufficient to fully validate generalization, especially for complex regions like urban areas. The overall structure does not appear novel\n\n**Response:**  \nAs an application-focused paper, our aim is to demonstrate the feasibility of climate-aware satellite image synthesis using established models. Our contribution lies in adapting them for multi-conditional generation with structured climate prompts and continuous variables. While our dataset includes some urban areas due to global sampling, our focus is on climate-sensitive landscapes where environmental change is linked to climate inputs. Urban dynamics, in contrast, are typically shaped by human decision-making and fall outside the primary scope of this work. For generalization, we refer to our detailed response to Reviewer WAR9, where we introduce a new temporal evaluation on 2023 and 2024 data and report consistent results.", "claims_and_evidence": "The authors claim that satellite images can be generated using text prompts containing spatial and climate information, and that the control image plays a crucial role in preserving the spatial structure. To support this claim, it would be beneficial to include experiments that analyze the generated images based on control images taken from different timelines of the same region.", "methods_and_evaluation_criteria": "The authors conducted experiments using a sufficiently large Sentinel-2 image dataset and evaluated the quality of the generated images based on various quantitative metrics such as FID, PSNR, and SSIM. These performance evaluation methods are appropriate. However, given the unique challenges of the satellite imagery domain, it would be valuable to include assessments that measure how closely the generated images match real-world conditions.", "theoretical_claims": "In this paper, no new theoretical proofs or claims are directly addressed. Instead, the model is built upon existing research, such as  Stable Diffusion, and its performance is verified through experiments.", "experimental_designs_or_analyses": "The number of experiments seems insufficient to fully validate the model's overall generalization capabilities. In particular, there is a lack of analysis for complex regions, such as urban areas, where the data and spatial structures are more challenging.", "relation_to_broader_scientific_literature": "This paper presents a new text prompt-based approach capable of reflecting various environmental conditions. The authors propose a method to generate conditioned satellite images by simultaneously incorporating spatial information and climate data.", "essential_references_not_discussed": "There do not appear to be any essential references missing.", "other_strengths_and_weaknesses": "Strength:\nThe paper introduces a deep learning model for generating realistic satellite images, which has potential for applications of satellite data.\n\nWeaknesses:\n1. Limited ablation studies on control images: The paper does not provide detailed ablation studies on the selection and impact of different control images. This analysis is essential to determine the robustness of the approach.\n2. Text prompt methodology: The authors incorporate text prompts to condition the generative process, however, there is insufficient discussion on how to concretely define effective text prompts, and the experiments evaluating which types of text prompts yield the best performance are lacking.\n3. Overall structure and generalizability: The overall structure does not appear particularly novel, and there are concerns regarding the generalizability of the approach", "other_comments_or_suggestions": "No additional comments.", "questions_for_authors": "Questions are embedded in the above section.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "qes9pU13qX", "reviewer": "Reviewer_vwzA", "summary": "This paper extends previous work on satellite image generation by introducing a larger dataset and considering two generation scenarios - text2img and ControlNet. Quantitative comparison shows superior performance only in FID but not other metrics (i.e., CLIP, SSIM, PSNR, LPIPS).", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your detailed and constructive feedback. In the following, we address the key points you raised.\n\n**Reviewer comment:**\n\nWhile the dataset's size is notable, the paper's primary contribution and the novelty of its proposed prompting setups remain unclear.\n\n**Response:**\nThe prompting strategy plays a key role in our approach. We tested several strategies for conditioning the model on climate and spatial context. These experiments are detailed in our response to Reviewer 54H1, where we present a breakdown of different prompt setups and their impact on generation quality. We kindly refer you to that section for a more in-depth explanation.\n\n**Reviewer comment:**\n\nOne way to demonstrate forecasting value would be to evaluate the model on hold-out or future satellite images conditioned on climate data.\n\n**Response:**\n\nWe conducted additional experiments to address this point. We evaluated all models on satellite images from 2023 and 2024, using the same 5,500 test locations as in the original setup. These years were not part of the training period (2017–2022), establishing a strict temporal split. The results (see below) show stable and consistent performance as in the other testings before, indicating that our models generalize well to unseen time periods. We will include this experiment, along with results for the single-image generation setup, in the final version of the paper. The 2023–2024 test data will be added to the dataset.\n\n**ControlNet – Year-wise Testset Results**\n\n|Year|CLIP↑|SSIM↑|PSNR↑|LPIPS↓|FID↓|Dataset share %|\n|-|-|-|-|-|-|-|\n|2017|0.36|0.40|13.33|0.61|102.14|8.5%|\n|2018|0.35|0.41|14.00|0.58|84.12|12.6%|\n|2019|0.36|0.41|13.90|0.58|89.52|12.4%|\n|2020|0.35|0.40|13.40|0.59|86.37|12.5%|\n|2021|0.33|0.40|13.73|0.58|83.30|12.1%|\n|2022|0.33|0.40|13.27|0.58|77.47|14.8%|\n|2023|0.35|0.43|14.09|0.59|93.21|9.8%|\n|2024|0.32|0.40|13.48|0.59|73.54|17.3%|\n\nWe want to briefly clarify the use of the Fréchet Inception Distance (FID). FID is known to be less reliable on small test sets, as it estimates distribution similarity based on feature mean and covariance from a pre-trained Inception network. Limited sample size can introduce noise and variance in these estimates. We interpret FID cautiously and emphasize the importance of using it alongside other metrics to ensure a more robust evaluation.\n\n**Reviewer comment:**\n\nA detailed analysis of when and why the model performs well or poorly would be helpful. Stratified evaluation (e.g., under extreme weather conditions) could offer valuable insights. I am very willing to raise my score is this experiment is provided.\n\n**Response:**\n\nThank you for this important suggestion. To address this, we performed additional stratified experiments focusing on extreme climate conditions, evaluating model performance across scenarios of high/low temperature, precipitation, and radiation. These weather extremes are closely tied to challenging land cover types (e.g., snowy forests, cloud-covered areas), which tend to be underrepresented in the training set and inherently harder to reconstruct. In contrast, warmer and drier regions — which are more common in the data — yield more accurate generations. The results below illustrate that low temperatures, high precipitation, and low radiation correspond to significantly lower visual fidelity and structural alignment, while high radiation and dry conditions lead to much better performance\n\n**Table 10: SD3_FT Performance Under Extreme Climate Conditions**\n\n|Condition|FID↓|SSIM↑|PSNR↑|LPIPS↓|Inception Score↑|CLIP Score↑|\n|-|-|-|-|-|-|-|\n|High Temperature|115.33|0.475|15.52|0.640|3.790|0.353|\n|Low Temperature|145.11|0.260|11.79|0.756|3.745|0.355|\n|High Precipitation|170.81|0.365|11.93|0.725|3.261|0.403|\n|Low Precipitation|85.63|0.430|15.10|0.665|4.185|0.339|\n|High Radiation|107.35|0.469|15.83|0.646|3.909|0.362|\n|Low Radiation|141.37|0.252|11.65|0.770|3.941|0.327|\n\nWe also provide a detailed per–land cover class analysis in our response to Reviewer WAR9 (ControlNet)\n\n**Reviewer comment:**  \nThe reviewer suggests citing additional related work, including SEN12MS-CR-TS, AllClear, DiffCR, CRS-Diff, and MetaEarth.\n\n**Response:**  \nWe acknowledge the growing number of generative models and datasets in EO and will take up these works in the discussion section of our paper and include references in the revised version. SEN12MS-CR-TS and AllClear focus on cloud removal using multi-modal Sentinel-1/2 data but are limited either temporally (e.g., only 2022) or regionally. DiffCR uses diffusion for cloud removal but lacks temporal and climate conditioning. CRS-Diff introduces multi-modal control but operates at different resolutions and without climate or multi-temporal information. MetaEarth enables large-scale image generation but relies solely on image and geographic metadata. In contrast, our work uniquely combines image and continuous climate conditioning for globally distributed, climate-aware satellite image synthesis.", "claims_and_evidence": "This paper presents EcoMapper, a large-scale satellite imagery dataset integrated with climate data. While the dataset's size is notable, the paper's primary contribution and the novelty of its proposed prompting setups remain unclear. The prompting methods, as presented, appear incremental. Furthermore, while potential applications like forecasting and scenario analysis are mentioned, the paper lacks concrete evidence demonstrating the utility of the generated imagery for these downstream tasks. Demonstrating the practical benefits of EcoMapper's generative capabilities would significantly enhance the paper's impact.", "methods_and_evaluation_criteria": "Since it is a generative modeling task, the presented evaluation is standard.", "theoretical_claims": "NA", "experimental_designs_or_analyses": "One way to \"convince\" the reader of the contribution of this work in forecasting is predicting hold-out images or future images. Say the model had stopped training for one month, and then I would wonder how good or bad this model is in forecasting future imageries, conditioning on future climate data (which is more mature and well-studied). In addition to this, a detailed analysis upon why the model performs good or bad can be very helpful for future work on satellite imgery generation. Stratified analysis such as under what conditions the model performs worse (e.g. extreme hot weather, or snowy scene) can also provide more insights to this community. I am very willing to raise my score is this experiment is provided.", "relation_to_broader_scientific_literature": "NA", "essential_references_not_discussed": "This work can be helpful for cloud removal in satellite imagery, and the reviewer can consider adding more references in this domain, such as SEN12MS-CR-TS (TGRS'22), AllClear (NeurIPS'24), or DiffCR (TGRS'24). \n\nIt would also be helpful if the authors discuss concurrent work such as CRS-Diff (TGRS'24) and MetaEarth (TGRS'24).", "other_strengths_and_weaknesses": "NA", "other_comments_or_suggestions": "NA", "questions_for_authors": "NA", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Lbhb2PvQ5Z", "reviewer": "Reviewer_oS3e", "summary": "This paper introduces two generative models for the generation of controllable satellite images. The models, based upon Stable Diffusion 3, enable the controlled generation of satellite images with several control types, including: Image-conditioned generation, spatiotemporal conditioning (location, date, land cover type, clouds), and climate-control (temperature, radiation, precipitation), and a novel combination of these factors. For training these models, the paper introduces a novel dataset of 2.7 million images based on Sentinel-2, spanning more than 100k geographical locations. The model is evaluated against several baselines, across relevant metrics for image generation quality and alignment. The contributions of this paper are said to advance climate-aware earth observation.\n\n## update after rebuttal\nI have read the rebuttal and other reviews. The authors have adequately addressed my concerns, and have provided a solid rebuttal overall. I am more positive about this paper and have increased my rating.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Reviewer comment:**  \nI am missing a reference to SatCLIP, which I find to be closely related to the contents of this paper.\n\n**Response:**  \n\nThank you for this helpful suggestion. We agree that SatCLIP (Klemmer et al., 2023) is highly relevant and will include a citation and brief discussion in the revised version of the paper. We considered using SatCLIP in our evaluation. However, the model is designed for multi-spectral Sentinel-2 inputs (13 channels), while our dataset consists of standard RGB images (3 channels). To make our images compatible, we would need to artificially pad the missing bands, which introduces noise and may result in embeddings that do not reflect SatCLIP’s original intent. We nonetheless tested SatCLIP for comparison, and the results support our concerns. As shown below, even the ground truth RGB images receive low (negative) SatCLIP scores, suggesting a mismatch between the model's input expectations and our dataset.\n\n**Table 7: SatCLIP Scores on RGB Imagery**\n| Model       | Avg. SatCLIP Score |\n|-|-|\n| SD3_FT_HR   | -0.0148            |\n| **SD3_FT**  | **-0.0105**        |\n| Diffsat_FT  | -0.0285            |\n| Ground Truth| -0.0171            |\nThese results suggest that SatCLIP is not well-suited for RGB-only satellite imagery, which is why we chose not to use it as a core evaluation metric. But we are happy to receive the reviewers feedback on this.\n\n**Reviewer comment:**  \n\nI remain unconvinced about the novelty of the model introduced by this paper. The conditioning mechanisms used in this paper are well studied in the literature, and using them for a new task does not constitute a novel technical contribution, in my opinion.\n\n**Response**\n\nThanks for the fair comment. As an application-focused paper, our aim is to demonstrate the feasibility of climate-aware satellite image synthesis using established models. Our contribution lies in adapting them for multi-conditional generation with structured climate prompts and continuous variables. We also added more discussion about the prompting strategy as outlined in the reply to reviewer 54H1, as we think this is a valuable contribution to the development of future climate- aware generative models for EO data.\n\n**Reviewer comment:**  \n\nI am puzzled by the lack of urban and rural but inhabited lands. This somewhat limits the scope of the dataset, in my opinion.\n\n**Response:**  \n\nThank you for raising this point. Our dataset was constructed through globally uniform random sampling and reflects the class distribution of the underlying land cover map, which defines only 16 categories—of which “Urban and Built-Up” is the only inhabited land type explicitly represented. We did not manually adjust class frequencies, and the share of urban areas (~0.7%) aligns with the global land surface covered by this class. While inhabited areas are important, our primary focus is on landscapes where climate conditions are a key driver of visual and environmental variation. In urban settings, land cover changes are often dominated by human interventions (e.g., new buildings, roads), which are less predictable from climate variables alone. For this reason, we consider urban and densely inhabited areas to be of secondary relevance in the current version of the dataset. \n\n**Reviewer question:**  \nWhat is the influence of scale and resolution in the model outputs when using image conditioning? Does it affect the outputs strongly?\n\n**Response:**  \n\nThank you for this technical question. Our ControlNet-based model operates at a fixed resolution of 512×512, which is a downscaled version of SD3’s native 1024×1024. During fine-tuning, we adjusted only the last layers, allowing the model to adapt its pre-trained high-resolution features to the lower resolution. In practice, using higher-resolution conditioning inputs could improve spatial detail and texture fidelity. However, since the model is trained entirely at 512×512, feeding 1024×1024 inputs without re-training could introduce inconsistencies or degrade visual quality due to the resolution mismatch. We agree that training at higher resolutions would likely enhance fine-grained spatial accuracy and believe it is a promising topic for future research activities.", "claims_and_evidence": "The paper makes two main claims. First that they introduced a dataset for the training of satellite image generation models. This claim is supported by evidence, provided particularly on the supplementary material, albeit more analyses would be welcome. Then, the paper introduces two different models generative models to solve the aforementioned tasks. These are presented in the paper, and relatively well evaluated, but I am not convinced that they are particularly novel in the context of generative models.", "methods_and_evaluation_criteria": "The methods and evaluation criteria make sense int he context of the problem, I have no signifcant concerns about this (see my comments in Experimental Designs Or Analyses for a more thorough analysis of the evaluation part of this section). \n\nNevertheless, I remain unconvinced about the novelty of the model introduced by this paper. The conditioning mechanisms used in this paper are well studied in the literature, and using them for a new task does not constitute a novel technical contribution, in my opinion.", "theoretical_claims": "There are no significant theoretical claims in this paper, it is very application-driven.", "experimental_designs_or_analyses": "I checked the experimental analyses in the paper. I found them to be sound in the context of the problem. The method is evaluated against several relevant baselines, across several metrics. Note that the metrics used in this paper are common for generative models (FID, PSNR, LPIPS, SSIM) and for text-conditioned models (CLIP). However, none of these metrics are particularly well-suited for the task this paper is trying to solve. I would suggest using SatCLIP as an additional metric for enhancing the quantitative results.\n\nFurther, the paper provides extensive qualitative results across different conditionings, showing the capabilities of the model. In any case, it would be interesting to do some sensitivity analysis, to study how much the images change in the dataset when the climate or temporal variables change, and compare with how much they change in the images generated by the model.", "relation_to_broader_scientific_literature": "To my eyes, this paper introduces a dataset and generative models for tasks that are somewhat novel, which are the climate-controllable generation of satellite imagery. This was underexplored in the literature.", "essential_references_not_discussed": "I am missing a reference to SatCLIP, which I find to be closely related to the contents of this paper. \n \nSatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery (Klemmer et al, 2023).", "other_strengths_and_weaknesses": "- The paper is well structured, well written and the quality of the figures is generally high.\n- Analysis of related work is sound.\n- My main concern with this paper is the lack of technical novelty. While the dataset may prove valuable, the methods introduced in this paper are hardly novel, and therefore the impact of this paper may be limited to using the dataset or the generative models as tools. This limits the scope of the paper quite strongly, in my opinion. Nevertheless, this is an applications-driven paper and, as such, I believe it could have some impact.", "other_comments_or_suggestions": "- I suggest the authors expand this dataset with more human-inhabited areas,", "questions_for_authors": "- What is the influence of scale and resolution in the model outputs when using image conditioning? Does it affect the outputs strongly?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper introduces a dataset of 2.7M set of Sentinel-2 RGB satellite images, and two Stable Diffusion-based models to text-to-image and ControlNet conditional generation, which are evaluated on the generation of satellite imagery for climate change visualisation applications.

Reviewers WAR9, oS3e and 54H1 found the application interesting and the dataset useful (confirmed by vwzA) as well as the paper well written and sound (oS3e).

Reviewer oS3e  had concern about technical novelty but agreed this was an application-driven paper. Reviewer WAR9 and vwzA found the application not clearly discussed and the real-world usefulness not demonstrated (clarified during rebuttal). Reviewer 54H1 found the ablations of control images insufficient and the text prompting not clearly evaluated (the authors provided ablation experiments to respond to these questions). 

After rebuttals and discussion, all reviewers but one raised their score, and given the average score of 3 I recommend acceptance.
Decision: Accept (poster)