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In-domain representation learning for remote sensing

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arxiv 1911.06721 v1 pith:4PF5F5DM submitted 2019-11-15 cs.CV

Maxim Neumann , Andre Susano Pinto , Xiaohua Zhai , Neil Houlsby This is my paper · ORCID
classification cs.CV
keywords remotesensinglearningrepresentationbaselinesdatasetsin-domainaccess
verification ladder T0 review T1 audit T2 compute T3 formal
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Given the importance of remote sensing, surprisingly little attention has been paid to it by the representation learning community. To address it and to establish baselines and a common evaluation protocol in this domain, we provide simplified access to 5 diverse remote sensing datasets in a standardized form. Specifically, we investigate in-domain representation learning to develop generic remote sensing representations and explore which characteristics are important for a dataset to be a good source for remote sensing representation learning. The established baselines achieve state-of-the-art performance on these datasets.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Improving Remote Sensing Classification using Topological Data Analysis and Convolutional Neural Networks

    cs.CV 2025-07 conditional novelty 6.0 of 10

    Fusing persistence homology descriptors with ResNet18 features reaches 99.33% on EuroSAT and 93.22% on RESISC45, improving both baselines and setting a claimed single-model EuroSAT record.

  2. Landsat-Bench: Datasets and Benchmarks for Landsat Foundation Models

    cs.CV 2025-06 conditional novelty 6.0 of 10

    Introduces Landsat-Bench, three Landsat 8 benchmarks derived from EuroSAT, BigEarthNet, and LC100, with baselines showing SSL4EO-L pretraining sometimes beats ImageNet.

  3. GeoVision Labeler: Zero-Shot Geospatial Classification with Vision and Language Models

    cs.CV 2025-05 conditional novelty 6.0 of 10

    A zero-shot geospatial classifier that turns satellite images into text descriptions and uses a language model to assign labels, plus an optional LLM-based hierarchical clustering for many-class datasets.

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