Seed-Driven Grid Adaptation Method: A Prior-Guided Active Learning Framework for Impervious Surface Mapping on the Qinghai–Xizang Plateau Using Google Satellite Embeddings.
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| Title: | Seed-Driven Grid Adaptation Method: A Prior-Guided Active Learning Framework for Impervious Surface Mapping on the Qinghai–Xizang Plateau Using Google Satellite Embeddings. |
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| Authors: | Zheng, Kaiyuan1,2 (AUTHOR), He, Guojin1,2,3 (AUTHOR) hegj@aircas.ac.cn, Yin, Ranyu1,3 (AUTHOR), Wang, Guizhou1,2 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1596. 38p. |
| Subjects: | Remote sensing, Spatial resolution, Machine learning, Urban growth |
| Geographic Terms: | Lhasa (China), Qinghai Sheng (China) |
| Abstract: | Highlights: What are the main findings? A Seed-Driven Grid Adaptation (SDGA) framework was developed to map impervious surfaces across the Qinghai–Xizang Plateau using only Google Satellite Embeddings, with a Prior-guided Hybrid Active Sampling (PHAS) strategy to automatically mine informative samples. The proposed method substantially improved mapping performance, increasing the F1-score in the Lhasa seed area from 65.02% to 82.22%, improving accuracy in about 67% of grids with a mean F1 gain of 0.1109, and producing a plateau-scale 10 m product with an overall F1-score of 0.8223. What are the implications of the main findings? Embedding features enable reliable impervious surface mapping in complex environments without direct use of spectral inputs. The framework reduces manual labeling effort and supports scalable mapping with potential for cross-region and cross-temporal applications. Impervious surfaces are an important land surface indicator of urbanization level and human activity intensity, playing a crucial role in urban development monitoring and ecological environment assessment. However, in complex high-altitude regions such as the Qinghai–Xizang Plateau, the identification accuracy of existing medium-resolution impervious surface products remains limited at the regional scale due to complex land surface backgrounds, sparse distributions of impervious surfaces, and their generally small spatial extent. To address this challenge, this study proposes a Seed-Driven Grid Adaptation (SDGA) framework for large-scale impervious surface mapping over the Qinghai–Xizang Plateau. The proposed method uses the Google Satellite Embeddings (GSE) dataset as the primary input features and incorporates a 10 m impervious surface prior (P10) derived from a 2 m high-resolution impervious surface product to provide spatial constraints. Based on this prior information, a Prior-guided Hybrid Active Sampling (PHAS) strategy is developed to automatically construct high-value training samples through uncertainty-based positive sample mining and cluster-based negative sample mining. The framework first builds an initial seed knowledge base in the Lhasa seed area and subsequently performs local adaptive expansion within a 2° × 2° grid system, enabling automated impervious surface mapping across the Qinghai–Xizang Plateau. Experimental results show that, with only a small number of initial samples, the PHAS strategy significantly improves model performance, increasing the F1 score for impervious surface identification in the Lhasa seed area from 65.02% to 82.22%. During the grid-level adaptation stage, approximately 67% of the grids achieved improved accuracy, with an average F1 score increase of 0.1109 across the study area. Ultimately, the SDGA framework produced a 10 m resolution impervious surface product for the Qinghai–Xizang Plateau (SDGA-ISC10m), achieving an overall F1 score of 0.8223. Compared with seven existing medium-resolution impervious surface datasets, the proposed method demonstrates improved recognition performance under complex plateau environments, particularly in detecting sparsely distributed and small-scale impervious surfaces. The results indicate that integrating remote sensing embedding features with active learning strategies can effectively reduce the need for manual annotation and provide a new technical pathway for large-scale impervious surface mapping in complex regions. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A Seed-Driven Grid Adaptation (SDGA) framework was developed to map impervious surfaces across the Qinghai–Xizang Plateau using only Google Satellite Embeddings, with a Prior-guided Hybrid Active Sampling (PHAS) strategy to automatically mine informative samples. The proposed method substantially improved mapping performance, increasing the F1-score in the Lhasa seed area from 65.02% to 82.22%, improving accuracy in about 67% of grids with a mean F1 gain of 0.1109, and producing a plateau-scale 10 m product with an overall F1-score of 0.8223. What are the implications of the main findings? Embedding features enable reliable impervious surface mapping in complex environments without direct use of spectral inputs. The framework reduces manual labeling effort and supports scalable mapping with potential for cross-region and cross-temporal applications. Impervious surfaces are an important land surface indicator of urbanization level and human activity intensity, playing a crucial role in urban development monitoring and ecological environment assessment. However, in complex high-altitude regions such as the Qinghai–Xizang Plateau, the identification accuracy of existing medium-resolution impervious surface products remains limited at the regional scale due to complex land surface backgrounds, sparse distributions of impervious surfaces, and their generally small spatial extent. To address this challenge, this study proposes a Seed-Driven Grid Adaptation (SDGA) framework for large-scale impervious surface mapping over the Qinghai–Xizang Plateau. The proposed method uses the Google Satellite Embeddings (GSE) dataset as the primary input features and incorporates a 10 m impervious surface prior (P10) derived from a 2 m high-resolution impervious surface product to provide spatial constraints. Based on this prior information, a Prior-guided Hybrid Active Sampling (PHAS) strategy is developed to automatically construct high-value training samples through uncertainty-based positive sample mining and cluster-based negative sample mining. The framework first builds an initial seed knowledge base in the Lhasa seed area and subsequently performs local adaptive expansion within a 2° × 2° grid system, enabling automated impervious surface mapping across the Qinghai–Xizang Plateau. Experimental results show that, with only a small number of initial samples, the PHAS strategy significantly improves model performance, increasing the F1 score for impervious surface identification in the Lhasa seed area from 65.02% to 82.22%. During the grid-level adaptation stage, approximately 67% of the grids achieved improved accuracy, with an average F1 score increase of 0.1109 across the study area. Ultimately, the SDGA framework produced a 10 m resolution impervious surface product for the Qinghai–Xizang Plateau (SDGA-ISC10m), achieving an overall F1 score of 0.8223. Compared with seven existing medium-resolution impervious surface datasets, the proposed method demonstrates improved recognition performance under complex plateau environments, particularly in detecting sparsely distributed and small-scale impervious surfaces. The results indicate that integrating remote sensing embedding features with active learning strategies can effectively reduce the need for manual annotation and provide a new technical pathway for large-scale impervious surface mapping in complex regions. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101596 |
