EcoWikiRS: Learning Ecological Representation of Satellite Images from Weak Supervision with Species Observations and Wikipedia

• URL: http://arxiv.org/abs/2504.19742v1
• Date: Mon, 28 Apr 2025 12:42:18 GMT
• Title: EcoWikiRS: Learning Ecological Representation of Satellite Images from Weak Supervision with Species Observations and Wikipedia
• Authors: Valerie Zermatten, Javiera Castillo-Navarro, Pallavi Jain, Devis Tuia, Diego Marcos,
• Abstract summary: We propose a method to predict ecological properties directly from remote sensing (RS) images by aligning them with species habitat descriptions.<n>We introduce the EcoWikiRS dataset, consisting of high-resolution aerial images, the corresponding geolocated species observations, and, for each species, the textual descriptions of their habitat from Wikipedia.<n>Our results show that our approach helps in understanding RS images in a more ecologically meaningful manner.
• Score: 8.80913094574943
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The presence of species provides key insights into the ecological properties of a location such as land cover, climatic conditions or even soil properties. We propose a method to predict such ecological properties directly from remote sensing (RS) images by aligning them with species habitat descriptions. We introduce the EcoWikiRS dataset, consisting of high-resolution aerial images, the corresponding geolocated species observations, and, for each species, the textual descriptions of their habitat from Wikipedia. EcoWikiRS offers a scalable way of supervision for RS vision language models (RS-VLMs) for ecology. This is a setting with weak and noisy supervision, where, for instance, some text may describe properties that are specific only to part of the species' niche or is irrelevant to a specific image. We tackle this by proposing WINCEL, a weighted version of the InfoNCE loss. We evaluate our model on the task of ecosystem zero-shot classification by following the habitat definitions from the European Nature Information System (EUNIS). Our results show that our approach helps in understanding RS images in a more ecologically meaningful manner. The code and the dataset are available at https://github.com/eceo-epfl/EcoWikiRS.

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