Fine-tuning of Geospatial Foundation Models for Aboveground Biomass Estimation

• URL: http://arxiv.org/abs/2406.19888v1
• Date: Fri, 28 Jun 2024 12:54:10 GMT
• Title: Fine-tuning of Geospatial Foundation Models for Aboveground Biomass Estimation
• Authors: Michal Muszynski, Levente Klein, Ademir Ferreira da Silva, Anjani Prasad Atluri, Carlos Gomes, Daniela Szwarcman, Gurkanwar Singh, Kewen Gu, Maciel Zortea, Naomi Simumba, Paolo Fraccaro, Shraddha Singh, Steve Meliksetian, Campbell Watson, Daiki Kimura, Harini Srinivasan,
• Abstract summary: Fine-tuning of a geospatial foundation model to estimate above-ground biomass has comparable performance to a U-Net trained from scratch. We also explore the transfer-learning capabilities of the models by fine-tuning on satellite imagery with sparse labels from different eco-regions in Brazil.
• Score: 2.3429628556845405
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Global vegetation structure mapping is critical for understanding the global carbon cycle and maximizing the efficacy of nature-based carbon sequestration initiatives. Moreover, vegetation structure mapping can help reduce the impacts of climate change by, for example, guiding actions to improve water security, increase biodiversity and reduce flood risk. Global satellite measurements provide an important set of observations for monitoring and managing deforestation and degradation of existing forests, natural forest regeneration, reforestation, biodiversity restoration, and the implementation of sustainable agricultural practices. In this paper, we explore the effectiveness of fine-tuning of a geospatial foundation model to estimate above-ground biomass (AGB) using space-borne data collected across different eco-regions in Brazil. The fine-tuned model architecture consisted of a Swin-B transformer as the encoder (i.e., backbone) and a single convolutional layer for the decoder head. All results were compared to a U-Net which was trained as the baseline model Experimental results of this sparse-label prediction task demonstrate that the fine-tuned geospatial foundation model with a frozen encoder has comparable performance to a U-Net trained from scratch. This is despite the fine-tuned model having 13 times less parameters requiring optimization, which saves both time and compute resources. Further, we explore the transfer-learning capabilities of the geospatial foundation models by fine-tuning on satellite imagery with sparse labels from different eco-regions in Brazil.

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