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Article

UAV Imagery-Based Classification Model for Atypical Traditional Village Landscapes and Their Spatial Distribution Pattern

1
College of Art and Design, Southwest Forestry University, Kunming 650224, China
2
Yunnan Forestry Technological College, Kunming 650224, China
3
College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China
*
Author to whom correspondence should be addressed.
Drones 2024, 8(7), 297; https://doi.org/10.3390/drones8070297
Submission received: 6 June 2024 / Revised: 2 July 2024 / Accepted: 2 July 2024 / Published: 4 July 2024

Abstract

For atypical traditional villages, their invaluable historical traces and cultural memories are preserved in the existing village landscapes. Rapid and accurate acquisition of the spatial information of various surface elements in a village is an important prerequisite for a scientific, reasonable, feasible planning and design scheme for conserving, progressing, and developing atypical villages. Taking Qianfeng Village as an example, this research proposes the atypical traditional village landscape classification model based on unmanned aerial vehicle (UAV) imagery (ATVLUI) by virtue of the UAV RGB images and the object-oriented fuzzy logic membership classification technique that extracts objects according to their spectrums, textures, geometries, and context relationships, aiming at precise extraction of atypical traditional village landscapes. Based on the landscape information, the landscape pattern indexes are calculated to explore the spatial distribution characteristics of different landscapes and analyze the current conditions of Qianfeng Village as the epitome of atypical traditional villages. Accordingly, suggestions for protecting, planning, and developing atypical villages are proposed. The results show that: (1) the ATVLUI boasts excellent identification for village landscapes in a complex scenario, with a classification accuracy for traditional structures of 84%, an overall accuracy of 93%, and a Kappa coefficient of 0.89. This model is proven superior to K-nearest neighbors (KNN), decision tree (DT), and random tree (RT); (2) according to the area and proportion calculations, the structures account for 33.94% of Qianfeng Village’s total area, in which 29.69% and 4.25% are modern and traditional structures, respectively. The number of traditional structures is 202, accounting for 13% of the total number of structures; (3) within the village, connectivity between and extension of the modern structures can be recognized, suggesting a trajectory where the traditional structures are being gradually substituted by modern ones. The ecological environment at the periphery of the village is favorable. The building-to-building common boundaries are long. The modern structures are densely distributed. The discretely distributed traditional structures gather as small clusters. In general, different structures are highly interlaced to form a fragmented distribution pattern.
Keywords: atypical traditional village; UAV; layered multi-feature; landscape; spatial pattern atypical traditional village; UAV; layered multi-feature; landscape; spatial pattern

Share and Cite

MDPI and ACS Style

Zheng, S.; Wei, L.; Yu, H.; Kou, W. UAV Imagery-Based Classification Model for Atypical Traditional Village Landscapes and Their Spatial Distribution Pattern. Drones 2024, 8, 297. https://doi.org/10.3390/drones8070297

AMA Style

Zheng S, Wei L, Yu H, Kou W. UAV Imagery-Based Classification Model for Atypical Traditional Village Landscapes and Their Spatial Distribution Pattern. Drones. 2024; 8(7):297. https://doi.org/10.3390/drones8070297

Chicago/Turabian Style

Zheng, Shaojiang, Lili Wei, Houjie Yu, and Weili Kou. 2024. "UAV Imagery-Based Classification Model for Atypical Traditional Village Landscapes and Their Spatial Distribution Pattern" Drones 8, no. 7: 297. https://doi.org/10.3390/drones8070297

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