Abstract
Geo-Foundation Models (GFMs), have proven effective in diverse downstream applications, including semantic segmentation, classification, and regression tasks. However, in case of flood mapping using Sen1Flood11 dataset as a downstream task, GFMs struggles to outperform the baseline U-Net, highlighting model's limitation in capturing critical local nuances. To address this, we present the Prithvi-Complementary Adaptive Fusion Encoder (CAFE), which integrate Prithvi GFM pretrained encoder with a parallel CNN residual branch enhanced by Convolutional Attention Modules (CAM). Prithvi-CAFE enables fast and efficient fine-tuning through adapters in Prithvi and performs multi-scale, multi-level fusion with CNN features, capturing critical local details while preserving long-range dependencies. We achieve state-of-the-art results on two comprehensive flood mapping datasets: Sen1Flood11 and FloodPlanet. On Sen1Flood11 test data, Prithvi-CAFE (IoU 83.41) outperforms the original Prithvi (IoU 82.50) and other major GFMs (TerraMind 82.90, DOFA 81.54, spectralGPT: 81.02). The improvement is even more pronounced on the hold-out test site, where Prithvi-CAFE achieves an IoU of 81.37 compared to the baseline U-Net (70.57) and original Prithvi (72.42). On FloodPlanet, Prithvi-CAFE also surpasses the baseline U-Net and other GFMs, achieving an IoU of 64.70 compared to U-Net (60.14), Terramind (62.33), DOFA (59.15) and Prithvi 2.0 (61.91). Our proposed simple yet effective Prithvi-CAFE demonstrates strong potential for improving segmentation tasks where multi-channel and multi-modal data provide complementary information and local details are critical. The code is released on \href{this https URL}{Prithvi-CAFE Github}
Abstract (translated)
地理基础模型（GFMs）在语义分割、分类和回归任务等不同下游应用中已被证明是有效的。然而，当使用Sen1Flood11数据集进行洪水制图这一特定任务时，GFMs的表现不如基线U-Net，这表明了模型在捕捉关键局部细节方面存在局限性。 为了解决这个问题，我们提出了Prithvi-互补自适应融合编码器（CAFE），它将预训练的Prithvi GFM编码器与增强有卷积注意模块（CAM）的平行CNN残差分支集成在一起。Prithvi-CAFE通过在Prithvi中使用适配器进行快速高效的微调，并且能够利用CNN特征实现多尺度、多层次融合，从而捕捉到关键局部细节同时保持长程依赖性。 我们在两个全面的洪水制图数据集（Sen1Flood11和FloodPlanet）上实现了最先进的成果。在Sen1Flood11测试数据集中，Prithvi-CAFE (IoU 83.41) 超过了原始Prithvi模型 (IoU 82.50)，以及其他的GFMs（TerraMind 82.90, DOFA 81.54, spectralGPT: 81.02）。这种改进在独立的测试站点上更为明显，其中Prithvi-CAFE实现了81.37的IoU值，而基线U-Net为70.57，原始Prithvi模型为72.42。在FloodPlanet数据集上，Prithvi-CAFE也优于基线U-Net和其他GFMs，在IoU指标上达到了64.70，相比之下，U-Net为60.14、TerraMind为62.33、DOFA为59.15和Prithvi 2.0为61.91。 我们提出的简单而有效的Prithvi-CAFE模型显示了在多通道及多模态数据提供互补信息且局部细节至关重要的分割任务中提升性能的巨大潜力。相关代码已发布至[Prithvi-CAFE Github](https://github.com/your-repo-url-here)（请将URL替换为实际的GitHub地址）。
URL
https://arxiv.org/abs/2601.02315
