Towards Label-Efficient Geospatial Foundation Model Adaptation via Positive-Unlabeled Learning for Building Damage Mapping

Hengwei Zhao; Qirui Wang; Zhuo Zheng; Xiaoyan Lu; Rusty Feagin; Wenzhe Jiao

cs.CVNov 5, 2026

Towards Label-Efficient Geospatial Foundation Model Adaptation via Positive-Unlabeled Learning for Building Damage Mapping

Authors: Hengwei Zhao, Qirui Wang, Zhuo Zheng, Xiaoyan Lu, Rusty Feagin, Wenzhe Jiao

Organizations: Department of Ecology and Conservation Biology, Texas A&M University, College Station, 77843, Texas, USA · Department of Computer Science, Stanford University, Stanford, 94305, CA, USA · Department of Mechanical Engineering, National University of Singapore, Singapore, 117575, Singapore

Abstract

Geospatial foundation models (GFMs) are reshaping remote sensing by shifting the field from task-specific modeling toward general-purpose geospatial intelligence. Mapping the damage to buildings stands to benefit substantially from this shift, as GFMs pretrained on large-scale Earth observation data provide generalizable geospatial representations that can facilitate effective responses to sudden-onset disasters. Although parameter-efficient fine-tuning (PEFT) facilitates GFM adaptation, parameter efficiency does not necessarily translate into label efficiency. Building damage mapping remains challenging due to scarce pre-existing labeled data or limited time for annotation. When weak supervision is introduced to alleviate the labeling bottleneck, we identify the difficulty of learning sufficiently discriminative representations as a key challenge for label-efficient GFM adaptation. To address this challenge, we propose a Positive-Unlabeled GFM Adaptation (PUMA) framework that requires annotations for only a small number of damaged buildings. More specifically, PUMA integrates non-contrastive weakly supervised representation refinement to mitigate the impact of noisy supervision on representation learning from a gradient perspective, and latent-state-based label disambiguation to enable reversible supervision refinement without requiring class prior estimation. Extensive experiments on disaster benchmark datasets from around the world demonstrated the effectiveness and robustness of PUMA. A real-world application to the 2025 Los Angeles wildfires further highlighted its potential to accurately map damage under highly limited supervision. More broadly, these findings suggest that weakly supervised PEFT offers a label-efficient pathway from GFMs to real-world applications.

Figures & tables

Panel of Figure 1 — Figure 1: t-SNE ( van der Maaten and Hinton, 2008 ) visualization of learned representations, representation distributions estimated using kernel density estimation (KDE), and decision boundaries produced by different methods on the hurricane-florence subset of the xBD dataset. VPT-deep was used as the PEFT strategy for all methods.

Figure 2: Spatial distribution of all disaster events considered in this paper.

Name	Disaster event	Event date	Number of images
Used for building damage classification and building localization evaluation
hurricane-florence	Hurricane Florence	Sep 10-19, 2018	1092
joplin-tornado	Joplin, MO Tornado	May 22, 2011	298
santa-rosa-wildfire	Santa Rosa Wildfires	Oct 8-31, 2017	754
Used only for training the building localization model
guatemala-volcano	Guatemala Fuego Volcano Eruption	Jun 3, 2018	56

Table 1: Summary statistics of the xBD dataset.

Figure 3: Overview of the proposed positive-unlabeled GFM adaptation framework.

Method	Tsunami		Hurricane		Tornado		Wildfire
	OA	F1	OA	F1	OA	F1	OA	F1
Training-from-scratch methods
nnPU	78.48 ±0.55	81.84 ±0.38	92.76 ±0.52	82.57 ±1.13	95.60 ±0.50	92.87 ±0.74	93.92 ±0.06	87.27 ±0.18
vPU	65.82 ±5.39	48.77 ±12.71	84.62 ±1.91	27.85 ±14.66	72.42 ±1.75	13.79 ±10.01	80.65 ±2.43	27.22 ±15.78
HolisticPU	80.67 ±1.89	78.50 ±2.85	51.90 ±30.25	43.68 ±16.45	82.16 ±18.10	77.98 ±16.52	56.19 ±33.00	54.72 ±28.19
WSC	86.41 ±0.95	85.83 ±1.21	92.59 ±1.23	81.97 ±2.20	95.69 ±0.72	92.92 ±1.02	95.05 ±0.26	89.78 ±0.37

Table 2: Quantitative comparison of different methods. The best result for each metric is highlighted in red.

Figure 4: Visual comparison of different methods on the Hurricane Florence subset of the xBD dataset. Damaged and undamaged buildings are shown in red and green , respectively.

Figure 5: Visual comparison of different methods on the Joplin Tornado subset of the xBD dataset. Damaged and undamaged buildings are shown in red and green , respectively.

Representation
Learning	Pseudo-label
Updating	Tsunami				Hurricane
		OA	F1	P	R	OA	F1	P	R
$\mathcal{L}_{\text{rep}}^{\text{self}}$		68.70 ±1.90	54.83 ±3.99	97.66 ±0.83	38.18 ±3.76	91.39 ±0.65	70.24 ±3.00	96.87 ±0.24	55.16 ±3.80
$\mathcal{L}_{\text{rep}}^{\text{sup}}$		65.32 ±8.56	45.60 ±18.94	97.78 ±0.25	31.22 ±17.58	92.72 ±1.03	76.10 ±4.53	96.18 ±1.05	63.17 ±6.44
$\mathcal{L}_{\text{rep}}^{\text{sqrt}}(k=1)$		67.14 ±4.50	51.02 ±10.06	97.73 ±0.67	34.92 ±8.99	93.53 ±0.13	79.36 ±0.35	96.58 ±0.75	67.35 ±0.15

Table 3: Ablation study of the proposed modules. The parameter

k

\mathcal{L}_{\text{rep}}^{\text{sqrt}}

was set to 9 by default.

Method	OA	F1	P	R
uPU	83.48 ±2.31	81.73 ±3.15	90.88 ±1.86	74.40 ±5.32
uPU+ $\mathcal{L}_{\text{rep}}^{\text{sqrt}}$	89.34 ±0.31	89.11 ±0.58	90.85 ±1.78	87.52 ±2.78
nnPU	88.18 ±1.82	87.48 ±2.07	92.67 ±1.04	82.87 ±3.12
nnPU+ $\mathcal{L}_{\text{rep}}^{\text{sqrt}}$	90.03 ±0.24	90.10 ±0.46	89.25 ±1.54	91.04 ±2.50

Table 4: Effect of representation refinement loss on existing PU learning methods.

Method		Tsunami		Hurricane
Method		OA	F1	OA	F1
Full fine-tuning		50.45 ±0.95	66.44 ±0.23	46.64 ±39.64	48.14 ±28.41
Linear probing		90.80 ±0.26	91.12 ±0.26	94.57 ±0.75	86.53 ±1.56
PEFT	BitFit	92.92 ±0.84	93.07 ±0.73	97.34 ±0.07	92.86 ±0.29
	VPT-shallow	90.83 ±0.70	91.11 ±0.57	95.81 ±0.15	89.35 ±0.41
	VPT-deep	93.63 ±0.32	93.71 ±0.33	97.32 ±0.60	92.73 ±1.80

Table 5: Performance of PUMA with different PEFT strategies.

Pre-trained
model	Backbone	Tsunami		Hurricane
		OA	F1	OA	F1
scratch	ViT-B	80.98 ±2.25	83.34 ±1.44	91.20 ±1.90	80.01 ±3.22
CLIP	ResNet-50	91.18 ±0.09	91.39 ±0.37	96.95 ±0.69	92.07 ±1.57
	ViT-B	93.79 ±0.57	93.85 ±0.49	97.53 ±0.30	93.26 ±0.81
LCLIP	ViT-B	93.76 ±0.75	93.83 ±0.69	97.46 ±0.29	93.12 ±0.86

Table 6: Performance of PUMA with different GFMs.

Disaster	Building Localization			Building Damage Classification
Event	AP Box	AP Mask	IoU	P Pixel	R Pixel	F1 Pixel	P	R	F1
Hurricane	56.7 ±0.2	52.2 ±0.1	77.8 ±0.6	79.75 ±0.26	88.97 ±0.26	84.11 ±0.26	75.89 ±0.34	84.63 ±0.38	80.02 ±0.36
Tornado	52.1 ±0.4	48.2 ±0.2	83.0 ±0.4	84.26 ±0.14	91.99 ±0.10	87.96 ±0.12	83.38 ±0.25	76.88 ±0.23	80.00 ±0.24
Wildfire	52.1 ±0.3	47.3 ±0.2	72.9 ±2.5	80.58 ±0.16	89.71 ±0.12	84.90 ±0.15	80.61 ±0.08	84.09 ±0.08	82.32 ±0.08

Table 7: Building localization and damage classification performance using automatically extracted building footprints.

Figure 6: Hyperparameter analyses of PUMA for the tsunami and hurricane events.

Figure 7: Building damage mapping in the Eaton subarea of the Los Angeles wildfires. The left column shows pre-disaster imagery with building footprints, and the right column shows post-disaster imagery with the corresponding damage mapping results. Damaged and undamaged buildings are shown in red and green , respectively.

Method	Eaton Subarea				Palisades Subarea
	OA	F1	P	R	OA	F1	P	R
$\|\mathcal{D}_{p}\|=50$ damaged buildings
Naive Supervision	94.03 ±0.76	76.41 ±3.69	90.11 ±0.92	66.40 ±5.02	50.63 ±2.23	38.46 ±4.26	98.70 ±0.93	23.93 ±3.28
Standard Supervision	91.73 ±0.74	77.54 ±1.47	64.34 ±2.20	97.63 ±0.75	92.87 ±1.08	94.26 ±0.93	98.26 ±0.29	90.58 ±1.80
PUMA	97.81 ±0.28	92.81 ±0.86	89.07 ±1.53	96.90 ±1.03	92.48 ±1.47	93.89 ±1.24	98.79 ±0.46	89.46 ±1.88
Full Supervision	97.60 ±0.62	92.30 ±1.85	87.16 ±3.12	98.11 ±0.34	97.44 ±0.04	98.01 ±0.03	98.26 ±0.42	97.77 ±0.44

Table 8: Building damage mapping results for the Eaton and Palisades subareas during the 2025 Los Angeles wildfires. Naive Supervision treats all unlabeled samples as negative; Standard Supervision additionally incorporates labeled undamaged buildings in equal number to the labeled damaged buildings; and Full Supervision assigns ground-truth labels to all unlabeled samples.

Figure 8: Building damage mapping in the Palisades subarea of the Los Angeles wildfires. The left column shows pre-disaster imagery with building footprints, and the right column shows post-disaster imagery with the corresponding damage mapping results. Damaged and undamaged buildings are shown in red and green , respectively.

Panel of Figure 9 — Figure 9: Label efficiency of PUMA compared with supervised PEFT methods in the Eaton subarea of the Los Angeles wildfires. A fixed set of 4,000 unlabeled buildings is used in all experiments, and $1\times$ corresponds to 100 annotated samples. (a) F1 scores under varying numbers of labeled damaged buildings. PUMA maintains performance close to Full Supervision and consistently outperforms Standard Supervision across annotation budgets. (b) PUMA requires only $1/40$ of the annotated samples used by Standard Supervision.

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Low-Rank Adaptation of Geospatial Foundation Models for Wildfire Mapping Using Sentinel-2 Data

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Wildfire burned-area mapping is essential for damage assessment, emissions modeling, and understanding fire-climate interactions across diverse ecological regions. Recent geospatial foundation models provide strong general-purpose representations for satellite imagery, yet there is still no clear understanding of how to efficiently adapt these models for downstream Earth observation tasks, particularly under geographic and temporal domain shift. This study evaluates three state-of-the-art Geospatial Foundation Models (GFMs) - Terramind, DINOv3, and Prithvi-v2 - for burned-area mapping across the United States and Canada using Sentinel-2 data. Leveraging 3,820 wildfire events from 2017-2023, we conduct spatial and temporal generalization tests across diverse biomes. We systematically compare full fine-tuning, decoder-only fine-tuning, and Low-Rank Adaptation (LoRA) for adapting each model. Across all experiments, LoRA provides the strongest cross-domain generalization while updating less than 1% of parameters, demonstrating a favorable trade-off between accuracy and efficiency. Prithvi-v2 with LoRA achieves the highest overall accuracy and the largest improvement compared to full fine-tuning. These findings indicate that geospatial foundation models, when adapted using lightweight parameter-efficient methods such as LoRA, offer a robust and scalable solution for large-scale burned-area mapping. Code is available at https://github.com/alishibli97/wildfire-lora-gfm.

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