Xt-Seca
An Efficient and Accurate XGBoost–Transformer Model for Urban Functional Zone Classification
Bibliographic Data
| ID | 22032802 |
|---|---|
| Authors | Xin Gao (0000-0002-0937-6288, China University of Geosciences), Xianmin Wang (0000-0003-3480-8780, China University of Geosciences, corresponding author), Li Cao (0000-0002-6866-4191, China University of Geosciences), Li Juan Cao (0009-0002-6775-6569, China University of Geosciences), Haixiang Guo (0000-0002-4274-3975, China University of Geosciences), Hai‐Xiang Guo (0000-0002-3410-4372, China University of Geosciences), Wenxue Chen (0000-0002-4148-3569, China University of Geosciences), Xing Zhai (0009-0001-8713-1550, China Geological Survey) |
| Year | 2025 |
| Volume | 14 |
| Issue | 8 |
| Pages | 290 |
| Publication date | 2025-07-25 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi14080290 |
| OpenAlex | W4412833359 |
| Language | EN |
| References cited | 35 |
The remote sensing classification of urban functional zones provides scientific support for urban planning, land resource optimization, and ecological environment protection. However, urban functional zone classification encounters significant challenges in accuracy and efficiency due to complicated image structures, ambiguous critical features, and high computational complexity. To tackle these challenges, this work proposes a novel XT-SECA algorithm employing a strengthened efficient channel attention mechanism (SECA) to integrate the feature-extraction XGBoost branch and the feature-enhancement Transformer feedforward branch. The SECA optimizes the feature-fusion process through dynamic pooling and adaptive convolution kernel strategies, reducing feature confusion between various functional zones. XT-SECA is characterized by sufficient learning of complex image structures, effective representation of significant features, and efficient computational performance. The Futian, Luohu, and Nanshan districts in Shenzhen City are selected to conduct urban functional zone classification by XT-SECA, and they feature administrative management, technological innovation, and commercial finance functions, respectively. XT-SECA can effectively distinguish diverse functional zones such as residential zones and public management and service zones, which are easily confused by current mainstream algorithms. Compared with the commonly adopted algorithms for urban functional zone classification, including Random Forest (RF), Long Short-Term Memory (LSTM) network, and Multi-Layer Perceptron (MLP), XT-SECA demonstrates significant advantages in terms of overall accuracy, precision, recall, F1-score, and Kappa coefficient, with an accuracy enhancement of 3.78%, 42.86%, and 44.17%, respectively. The Kappa coefficient is increased by 4.53%, 51.28%, and 52.73%, respectively
Data mining · Machine learning · Computer Science · Human Mobility and Location-Based Analysis · Land Use and Ecosystem Services · Remote-Sensing Image Classification · Artificial Intelligence
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| Citation velocity | historical |
|---|---|
| Highly cited | No |