Mapping on a Budget: Optimizing Spatial Data Collection for ML

• URL: http://arxiv.org/abs/2509.03749v1
• Date: Wed, 03 Sep 2025 22:24:55 GMT
• Title: Mapping on a Budget: Optimizing Spatial Data Collection for ML
• Authors: Livia Betti, Farooq Sanni, Gnouyaro Sogoyou, Togbe Agbagla, Cullen Molitor, Tamma Carleton, Esther Rolf,
• Abstract summary: In applications across agriculture, ecology, and human development, machine learning with satellite imagery (SatML) is limited by the sparsity of labeled training data.<n>We present the first problem formulation for the optimization of spatial training data in the presence of heterogeneous data collection costs.
• Score: 4.026605636117829
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In applications across agriculture, ecology, and human development, machine learning with satellite imagery (SatML) is limited by the sparsity of labeled training data. While satellite data cover the globe, labeled training datasets for SatML are often small, spatially clustered, and collected for other purposes (e.g., administrative surveys or field measurements). Despite the pervasiveness of this issue in practice, past SatML research has largely focused on new model architectures and training algorithms to handle scarce training data, rather than modeling data conditions directly. This leaves scientists and policymakers who wish to use SatML for large-scale monitoring uncertain about whether and how to collect additional data to maximize performance. Here, we present the first problem formulation for the optimization of spatial training data in the presence of heterogeneous data collection costs and realistic budget constraints, as well as novel methods for addressing this problem. In experiments simulating different problem settings across three continents and four tasks, our strategies reveal substantial gains from sample optimization. Further experiments delineate settings for which optimized sampling is particularly effective. The problem formulation and methods we introduce are designed to generalize across application domains for SatML; we put special emphasis on a specific problem setting where our coauthors can immediately use our findings to augment clustered agricultural surveys for SatML monitoring in Togo.

Related papers

• Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMs [66.63911043019294]
  Data preparation aims to denoise raw datasets, uncover cross-dataset relationships, and extract valuable insights from them.<n>This paper focuses on the use of LLM techniques to prepare data for diverse downstream tasks.<n>We introduce a task-centric taxonomy that organizes the field into three major tasks: data cleaning, standardization, error processing, imputation, data integration, and data enrichment.
  arXiv  Detail & Related papers  (2026-01-22T12:02:45Z)
• Learn More, Forget Less: A Gradient-Aware Data Selection Approach for LLM [51.21051698747157]
  We propose a self-adaptive gradient-aware data selection approach (GrADS) for supervised fine-tuning of large language models (LLMs)<n>Specifically, we design self-guided criteria that leverage the magnitude and statistical distribution of gradients to prioritize examples that contribute the most to the model's learning process.<n>Through extensive experimentation with various LLMs across diverse domains such as medicine, law, and finance, GrADS has demonstrated significant efficiency and cost-effectiveness.
  arXiv  Detail & Related papers  (2025-11-07T08:34:50Z)
• Can Large Language Models Integrate Spatial Data? Empirical Insights into Reasoning Strengths and Computational Weaknesses [11.330846631937671]
  We explore the application of large language models (LLMs) to empower domain experts in integrating large, heterogeneous, and noisy urban spatial datasets.<n>We show that while LLMs exhibit spatial reasoning capabilities, they struggle to connect the macro-scale environment with the relevant computational geometry tasks.<n>We then adapt a review-and-refine method, which proves remarkably effective in correcting erroneous initial responses while preserving accurate responses.
  arXiv  Detail & Related papers  (2025-08-07T03:44:20Z)
• Using Multiple Input Modalities Can Improve Data-Efficiency and O.O.D. Generalization for ML with Satellite Imagery [3.3964392722361785]
  A large majority of machine learning models trained on satellite imagery (SatML) are designed primarily for optical input modalities such as multi-spectral satellite imagery.<n>We generate augmented versions of SatML benchmark tasks by appending additional geographic data layers to datasets spanning classification, regression, and segmentation.<n>We find that fusing additional geographic inputs with optical imagery can significantly improve SatML model performance.
  arXiv  Detail & Related papers  (2025-07-15T22:57:29Z)
• Mitigating Forgetting in LLM Fine-Tuning via Low-Perplexity Token Learning [61.99353167168545]
  We show that fine-tuning with LLM-generated data improves target task performance and reduces non-target task degradation.<n>This is the first work to provide an empirical explanation based on token perplexity reduction to mitigate catastrophic forgetting in LLMs after fine-tuning.
  arXiv  Detail & Related papers  (2025-01-24T08:18:56Z)
• Advancing ALS Applications with Large-Scale Pre-training: Dataset Development and Downstream Assessment [6.606615641354963]
  The pre-training and fine-tuning paradigm has revolutionized satellite remote sensing applications.<n>We construct a large-scale ALS point cloud dataset and evaluate its impact on downstream applications.<n>Our results show that the pre-trained models significantly outperform their scratch counterparts across all downstream tasks.
  arXiv  Detail & Related papers  (2025-01-09T09:21:09Z)
• Retrieval-Augmented Data Augmentation for Low-Resource Domain Tasks [66.87070857705994]
  In low-resource settings, the amount of seed data samples to use for data augmentation is very small. We propose a novel method that augments training data by incorporating a wealth of examples from other datasets. This approach can ensure that the generated data is not only relevant but also more diverse than what could be achieved using the limited seed data alone.
  arXiv  Detail & Related papers  (2024-02-21T02:45:46Z)
• How to Train Data-Efficient LLMs [56.41105687693619]
  We study data-efficient approaches for pre-training language models (LLMs) We find that Ask-LLM and Density sampling are the best methods in their respective categories. In our comparison of 19 samplers, involving hundreds of evaluation tasks and pre-training runs, we find that Ask-LLM and Density are the best methods in their respective categories.
  arXiv  Detail & Related papers  (2024-02-15T02:27:57Z)
• Simulation-Enhanced Data Augmentation for Machine Learning Pathloss Prediction [9.664420734674088]
  This paper introduces a novel simulation-enhanced data augmentation method for machine learning pathloss prediction. Our method integrates synthetic data generated from a cellular coverage simulator and independently collected real-world datasets. The integration of synthetic data significantly improves the generalizability of the model in different environments.
  arXiv  Detail & Related papers  (2024-02-03T00:38:08Z)
• Minimally Supervised Learning using Topological Projections in Self-Organizing Maps [55.31182147885694]
  We introduce a semi-supervised learning approach based on topological projections in self-organizing maps (SOMs) Our proposed method first trains SOMs on unlabeled data and then a minimal number of available labeled data points are assigned to key best matching units (BMU) Our results indicate that the proposed minimally supervised model significantly outperforms traditional regression techniques.
  arXiv  Detail & Related papers  (2024-01-12T22:51:48Z)
• Building Manufacturing Deep Learning Models with Minimal and Imbalanced Training Data Using Domain Adaptation and Data Augmentation [15.333573151694576]
  We propose a novel domain adaptation (DA) approach to address the problem of labeled training data scarcity for a target learning task. Our approach works for scenarios where the source dataset and the dataset available for the target learning task have same or different feature spaces. We evaluate our combined approach using image data for wafer defect prediction.
  arXiv  Detail & Related papers  (2023-05-31T21:45:34Z)

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.