Geospatial Foundation Models to Enable Progress on Sustainable Development Goals

• URL: http://arxiv.org/abs/2505.24528v1
• Date: Fri, 30 May 2025 12:36:38 GMT
• Title: Geospatial Foundation Models to Enable Progress on Sustainable Development Goals
• Authors: Pedram Ghamisi, Weikang Yu, Xiaokang Zhang, Aldino Rizaldy, Jian Wang, Chufeng Zhou, Richard Gloaguen, Gustau Camps-Valls,
• Abstract summary: Foundation Models (FMs) are large-scale, pre-trained AI systems that have revolutionized natural language processing and computer vision.<n>This study provides a rigorous, interdisciplinary assessment of geospatial FMs and offers critical insights into their role in attaining sustainability goals.
• Score: 18.086843224361644
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Foundation Models (FMs) are large-scale, pre-trained AI systems that have revolutionized natural language processing and computer vision, and are now advancing geospatial analysis and Earth Observation (EO). They promise improved generalization across tasks, scalability, and efficient adaptation with minimal labeled data. However, despite the rapid proliferation of geospatial FMs, their real-world utility and alignment with global sustainability goals remain underexplored. We introduce SustainFM, a comprehensive benchmarking framework grounded in the 17 Sustainable Development Goals with extremely diverse tasks ranging from asset wealth prediction to environmental hazard detection. This study provides a rigorous, interdisciplinary assessment of geospatial FMs and offers critical insights into their role in attaining sustainability goals. Our findings show: (1) While not universally superior, FMs often outperform traditional approaches across diverse tasks and datasets. (2) Evaluating FMs should go beyond accuracy to include transferability, generalization, and energy efficiency as key criteria for their responsible use. (3) FMs enable scalable, SDG-grounded solutions, offering broad utility for tackling complex sustainability challenges. Critically, we advocate for a paradigm shift from model-centric development to impact-driven deployment, and emphasize metrics such as energy efficiency, robustness to domain shifts, and ethical considerations.

Related papers

• From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion [10.054868326253784]
  Quantitative remote sensing inversion aims to estimate continuous surface variables-such as biomass, vegetation indices, and evapotranspiration-from satellite observations.<n>Traditional physics-based paradigms are giving way to data-driven and foundation model (FM)-based approaches.
  arXiv  Detail & Related papers  (2025-07-11T23:57:00Z)
• SEEA-R1: Tree-Structured Reinforcement Fine-Tuning for Self-Evolving Embodied Agents [31.726927520069616]
  Self-Evolving Embodied Agents-R1, or SEEA-R1, is the first reinforcement fine-tuning framework designed for self-evolving embodied agents.<n>It converts sparse delayed rewards into denser intermediate signals that improve multi-step reasoning.<n>It generalizes reward estimation across tasks and scenes, supporting autonomous adaptation and reward-driven self-evolution.
  arXiv  Detail & Related papers  (2025-06-26T18:00:07Z)
• Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction [64.4881275941927]
  We present the first comprehensive evaluation of trustworthiness in a continental-scale multi-task LSTM model.<n>Our investigation uncovers systematic patterns of model performance disparities linked to basin characteristics.<n>This work serves as a timely call to action for advancing trustworthy data-driven methods for water resources management.
  arXiv  Detail & Related papers  (2025-03-13T01:50:50Z)
• Large Language Models Post-training: Surveying Techniques from Alignment to Reasoning [185.51013463503946]
  Large Language Models (LLMs) have fundamentally transformed natural language processing, making them indispensable across domains ranging from conversational systems to scientific exploration.<n>These challenges necessitate advanced post-training language models (PoLMs) to address shortcomings, such as restricted reasoning capacities, ethical uncertainties, and suboptimal domain-specific performance.<n>This paper presents the first comprehensive survey of PoLMs, systematically tracing their evolution across five core paradigms: Fine-tuning, which enhances task-specific accuracy; Alignment, which ensures ethical coherence and alignment with human preferences; Reasoning, which advances multi-step inference despite challenges in reward design; Integration and Adaptation, which
  arXiv  Detail & Related papers  (2025-03-08T05:41:42Z)
• Ten Challenging Problems in Federated Foundation Models [55.343738234307544]
  Federated Foundation Models (FedFMs) represent a distributed learning paradigm that fuses general competences of foundation models as well as privacy-preserving capabilities of federated learning.<n>This paper provides a comprehensive summary of the ten challenging problems inherent in FedFMs, encompassing foundational theory, utilization of private data, continual learning, unlearning, Non-IID and graph data, bidirectional knowledge transfer, incentive mechanism design, game mechanism design, model watermarking, and efficiency.
  arXiv  Detail & Related papers  (2025-02-14T04:01:15Z)
• Spatio-Temporal Foundation Models: Vision, Challenges, and Opportunities [48.45951497996322]
  Foundation models (STFMs) have revolutionized artificial intelligence, setting new benchmarks in performance and enabling transformative capabilities across a wide range of vision and language tasks.<n>In this paper, we articulate a vision for the future of STFMs, outlining their essential characteristics and generalization capabilities necessary for broad applicability.<n>We explore potential opportunities and directions to advance research towards the aim of effective and broadly applicable STFMs.
  arXiv  Detail & Related papers  (2025-01-15T08:52:28Z)
• Foundation Models for Remote Sensing and Earth Observation: A Survey [101.77425018347557]
  This survey systematically reviews the emerging field of Remote Sensing Foundation Models (RSFMs) It begins with an outline of their motivation and background, followed by an introduction of their foundational concepts. We benchmark these models against publicly available datasets, discuss existing challenges, and propose future research directions.
  arXiv  Detail & Related papers  (2024-10-22T01:08:21Z)
• When Geoscience Meets Foundation Models: Towards General Geoscience Artificial Intelligence System [6.445323648941926]
  Geoscience foundation models (GFMs) are a paradigm-shifting solution, integrating extensive cross-disciplinary data to enhance the simulation and understanding of Earth system dynamics. The unique strengths of GFMs include flexible task specification, diverse input-output capabilities, and multi-modal knowledge representation. This review offers a comprehensive overview of emerging geoscientific research paradigms, emphasizing the untapped opportunities at the intersection of advanced AI techniques and geoscience.
  arXiv  Detail & Related papers  (2023-09-13T08:44:09Z)
• SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning [63.192289553021816]
  Progress toward the United Nations Sustainable Development Goals has been hindered by a lack of data on key environmental and socioeconomic indicators. Recent advances in machine learning have made it possible to utilize abundant, frequently-updated, and globally available data, such as from satellites or social media. In this paper, we introduce SustainBench, a collection of 15 benchmark tasks across 7 SDGs.
  arXiv  Detail & Related papers  (2021-11-08T18:59:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.