Please wait a minute...
 
Remote Sensing for Natural Resources    2025, Vol. 37 Issue (4) : 48-57     DOI: 10.6046/zrzyyg.2024110
|
Landslide identification based on an improved YOLOv7 model: A case study of the Baige area
LIU Haoran1,2(), YAN Tianxiao1,2, ZHU Yueqin2(), WANG Yanping1, CHEN Zuyi1, YANG Zhaoying3, ZHU Haomeng4
1. Institute of Disaster Prevention Science and Technology, Langfang 065201, China
2. National Institute of Natural Disaster Prevention, Ministry of Emergency Management of China, Beijing 100085, China
3. China Aero Geophysical Survey and Remote Sensing Center, Beijing 100083, China
4. Zhejiang Institute of Geosciences, Hangzhou 310000, China
Download: PDF(6008 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

Landslide identification has always been a research topic in the study of geological disasters, playing a significant role in emergency rescue and command. To address the limitations in landslide identification, such as missed/false detection, and low identification accuracy, this study proposed an improved YOLOv7 model that enables simultaneous object detection and image segmentation for landslides. The improved model optimized its core network by integrating data, adding the convolutional block attention module (CBAM), and changing the intersection over union (IoU) loss function. Its effectiveness was verified using the landslide dataset of Bijie City, Guizhou Province, and the 0.2 m high-resolution digital orthophoto map (DOM) of historical landslides in Sichuan Province. The results indicate that the improved model performed well in landslide detection and segmentation, achieving more efficient and accurate landslide identification compared to the conventional YOLOv7 model, and other prevailing models like Fast RCNN and Mask RCNN. Taking the Baige area in Sichuan Province as an example, this model can effectively enhance the automation level of landslide disaster information acquisition while improving accuracy.
Keywords object detection      image segmentation      YOLOv7      landslide identification      remote sensing image     
ZTFLH:  TP79  
Issue Date: 03 September 2025
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Haoran LIU
Tianxiao YAN
Yueqin ZHU
Yanping WANG
Zuyi CHEN
Zhaoying YANG
Haomeng ZHU
Cite this article:   
Haoran LIU,Tianxiao YAN,Yueqin ZHU, et al. Landslide identification based on an improved YOLOv7 model: A case study of the Baige area[J]. Remote Sensing for Natural Resources, 2025, 37(4): 48-57.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2024110     OR     https://www.gtzyyg.com/EN/Y2025/V37/I4/48
Fig.1  Distribution map of landslide disasters in Baige area, Sichuan Province and surrounding areas
Fig.2  Sample data expansion effect
Fig.3  YOLOv7 network structure diagram
Fig.4  Schematic diagram of MPDIoU
Fig.5  Channel attention module network architecture
Fig.6  Spatial attention module network architecture
Fig.7  CBAM network architecture
模块 改进策略 精确率/% 召回率/% mAP@0.5/%
MPDIoU CBAM DEM
YOLOv7-segment 35.2 83.2 73.4 75.9
YOLOv7-segment-A √ 36.3 84.7 74.5 78.2
YOLOv7-segment-B √ 36.9 85.1 75.6 79.5
YOLOv7-segment-C √ 35.2 86.3 76.1 78.2
本文方法 √ √ √ 37.9 89.4 79.8 83.5
Tab.1  Various improved ablation experiments
Fig.8  Chart of mAP@0.5 curve comparison of each mode
算法模型 mAP@0.5/% 参数量/MB GFLOPs/GB
Mask R-CNN 75.4 45.8 66.55
YOLOv5L-seg 75.9 46.56 109.60
Faster-RCNN 70.6 41.00 241.40
YOLACT 68.7 47.70 126.80
YOLOv7-seg 79.8 38.27 143.20
本文算法 83.5 37.89 141.50
Tab.2  Comparison of different algorithms
Fig.9  Comparison effect before and after algorithm improvement
Fig.10  Missing images
Fig.11  Misdetected images
[1] 谭永杰, 刘荣梅, 朱月琴, 等. 论地质大数据的特点与发展方向[J]. 时空信息学报, 2023, 30(3):313-320.
[1] Tan Y J, Liu R M, Zhu Y Q, et al. On the characteristics and development directions of geological big data[J]. Journal of Spatio-Temporal Information, 2023, 30(3):313-320.
[2] 张潇远, 苏巧梅, 赵财胜, 等. 一种利用贝叶斯算法优化XGBoost的滑坡易发性评价方法[J]. 测绘科学, 2023, 48(6):140-150.
[2] Zhang X Y, Su Q M, Zhao C S, et al. A landslide susceptibility evaluation method using Bayesian algorithm to optimize XGBoost[J]. Science of Surveying and Mapping, 2023, 48(6):140-150.
[3] 康孟羽, 朱月琴, 陈晨, 等. 基于多元非线性回归和BP神经网络的滑坡滑动距离预测模型研究[J]. 地质通报, 2022, 41(12):2281-2289.
[3] Kang M Y, Zhu Y Q, Chen C, et al. Research on landslide sliding distance prediction model based on multiple nonlinear regression and BP neural network[J]. Geological Bulletin of China, 2022, 41(12):2281-2289.
[4] 洪韬, 朱月琴, 郭艳军, 等. 基于XR技术的交互式地质场景模型构建方法研究[J]. 地质通报, 2023, 42(s1):469-478.
[4] Hong T, Zhu Y Q, Guo Y J, et al. Research on interactive geological scene model construction method based on XR technology[J]. Geological Bulletin of China, 2023, 42(s1):469-478.
[5] Messeri A, Morabito M, Messeri G, et al. Weather-related flood and landslide damage:A risk index for Italian regions[J]. PLoS One, 2015, 10(12):e0144468.
[6] Chigira M, Yagi H. Geological and geomorphological characteristics of landslides triggered by the 2004 Mid Niigta prefecture earthquake in Japan[J]. Engineering Geology, 2006, 82(4):202-221.
[7] Nichol J, Wong M S. Satellite remote sensing for detailed landslide inventories using change detection and image fusion[J]. International Journal of Remote Sensing, 2005, 26(9):1913-1926.
[8] Hang H T, Tung H, Hoa P D, et al. Spatial prediction of landslides along National Highway-6,Hoa Binh Province,Vietnam using novel hybrid models[J]. Geocarto International, 2022, 37(18):5201-5226.
[9] Lei T, Zhang Y, Lyu Z, et al. Landslide inventory mapping from bitemporal images using deep convolutional neural networks[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(6):982-986.
[10] Ji S, Yu D, Shen C, et al. Landslide detection from an open satellite imagery and digital elevation model dataset using attention boosted convolutional neural networks[J]. Landslides, 2020, 17(6):1337-1352.
[11] Barlow J, Martin Y, Franklin S E. Detecting translational landslide scars using segmentation of Landsat ETM+ and DEM data in the northern Cascade Mountains,British Columbia[J]. Canadian Journal of Remote Sensing, 2003, 29(4):510-517.
[12] 杨昭颖, 韩灵怡, 郑向向, 等. 基于卷积神经网络的遥感影像及DEM滑坡识别——以黄土滑坡为例[J]. 自然资源遥感, 2022, 34(2):224-230.doi:10.6046/zrzyyg.2021204.
[12] Yang Z Y, Han L Y, Zheng X X, et al. Landslide identification using remote sensing images and DEM based on convolutional neural network:A case study of loess landslide[J]. Remote Sensing for Natural Resources, 2022, 34(2):224-230.doi:10.6046/zrzyyg.2021204.
[13] Ding A, Zhang Q, Zhou X, et al. Automatic recognition of landslide based on CNN and texture change detection[C]// 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC).November 11-13,2016,Wuhan,China.IEEE, 2016: 444-448.
[14] Wang C Y, Bochkovskiy A, Liao H Y M. YOLOv7:Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[EB/OL]. 2022: 2207.02696v1. https://arxiv.org/abs/2207.02696v1.
url: https://arxiv.org/abs/2207.02696v1
[15] 曾超, 曹振宇, 苏凤环, 等. 四川及周边滑坡泥石流灾害高精度航空影像及解译数据集(2008—2020年)[J]. 中国科学数据, 2022, 7(2):195-205.
[15] Zeng C, Cao Z Y, Su F H, et al. A dataset of high-precision aerial imagery and interpretation of landslide and debris flow disaster in Sichuan and surrounding areas between 2008 and 2020[J]. China Scientific Data, 2022, 7(2):195-205.
[16] 黄震, 朱华波, 陶友瑞. 基于YOLOv5的打磨表面质量快速评价方法[J]. 智能计算机与应用, 2022, 12(11):247-252.
[16] Huang Z, Zhu H B, Tao Y R. Rapid evaluation method of polished surface quality based on YOLOv5[J]. Intelligent Computer and Applications, 2022, 12(11):247-252.
[17] 宋敏毓, 陈力荣, 梁建安, 等. 轻量化改进网络的实时光纤端面缺陷检测模型[J]. 激光与光电子学进展, 2022, 59(24):2415006.
[17] Song M Y, Chen L R, Liang J A, et al. Real-time optical fiber end surface defects detection model based on lightweight improved network[J]. Laser and Optoelectronics Progress, 2022, 59(24):2415006.
[18] He K, Gkioxari G, Dollár P, et al. Mask R-CNN[C]// 2017 IEEE International Conference on Computer Vision (ICCV).October 22-29,2017,Venice,Italy.IEEE, 2017: 2980-2988.
[19] Jocher G. Yolov5[EB/OL].(2020-08-10)[2023-04-10]. https://github.com/ultralytics/yolo-v5.
url: https://github.com/ultralytics/yolo-v5
[20] Ren S, He K, Girshick R, et al. Faster R-CNN:Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6):1137-1149.
[21] Bolya D, Zhou C, Xiao F, et al. YOLACT:Real-time instance segmentation[C]// 2019 IEEE/CVF International Conference on Computer Vision (ICCV).October 27 - November 2,2019,Seoul,Korea (South).IEEE, 2019: 9156-9165.
[1] XU Ziyao, YANG Wu, SHI Xiaolong. Small target detection in remote sensing images based on lightweight YOLOv7-tiny[J]. Remote Sensing for Natural Resources, 2025, 37(4): 1-11.
[2] WANG Xinshuang, ZHAO Yehe, LIU Jiange, SUN Xin, ZHANG Yongzhen, MAO Dehua. Remote sensing-based assessment of wetland restoration potential in important wetland reserves along the Silk Road[J]. Remote Sensing for Natural Resources, 2025, 37(4): 204-211.
[3] ZHANG Qiao, CAO Zhicheng, SHEN Yang, WANG Zhoufeng, WANG Chengwu, XU Jiaxin. A method combining the siamese inverted residual structure with self-attention enhancement for change detection based on remote sensing images[J]. Remote Sensing for Natural Resources, 2025, 37(3): 85-94.
[4] GUO Wei, LI Yu, JIN Haibo. Detecting ships from SAR images based on high-dimensional contextual attention and dual receptive field enhancement[J]. Remote Sensing for Natural Resources, 2025, 37(3): 104-112.
[5] HE Xiaojun, LUO Jie. Semantic segmentation of high-resolution remote sensing images based on context- and class-aware feature fusion[J]. Remote Sensing for Natural Resources, 2025, 37(2): 1-10.
[6] PANG Min. An intelligent platform for extracting patches from multisource domestic satellite images and its application[J]. Remote Sensing for Natural Resources, 2025, 37(2): 148-154.
[7] NIE Shiyin, LIU Yansong, LI Huiling, XUE Kailun, SHEN Duheng, HE Boyu. Identification and classification of land types of alpine wetlands based on spectral coupling[J]. Remote Sensing for Natural Resources, 2025, 37(2): 204-211.
[8] DENG Jianming, YAO Hang, FU Bolin, GU Sen, TANG Jie, GAN Yuanyuan. Monitoring the spatiotemporal dynamics of mangrove forests in Beibu Gulf, Guangxi Zhuang Autonomous Region, China, using Google Earth Engine and time-series active and passive remote sensing images[J]. Remote Sensing for Natural Resources, 2025, 37(2): 235-245.
[9] XU Xinyu, LI Xiaojun, GE Junfei, LI Yikun. A multispectral image pansharpening algorithm based on nonsubsampled contourlet transform (NSCT) combined with a guided filter[J]. Remote Sensing for Natural Resources, 2025, 37(1): 24-30.
[10] WANG Tixin, YANG Jinzhong, XING Yu, WANG Kaijian. A method for automatic mapping of the remote sensing monitoring results of national nature reserves based on ArcPy and map optimization[J]. Remote Sensing for Natural Resources, 2025, 37(1): 252-259.
[11] CHEN Jiaxue, XIAO Dongsheng, CHEN Hongyu. A boundary guidance and cross-scale information interaction network for water body extraction from remote sensing images[J]. Remote Sensing for Natural Resources, 2025, 37(1): 15-23.
[12] QU Haicheng, LIANG Xu. Building extraction from high-resolution images using a hybrid attention mechanism combined with multi-scale feature enhancement[J]. Remote Sensing for Natural Resources, 2024, 36(4): 107-116.
[13] PAN Junjie, SHEN Li, YAN Xin, NIE Xin, DONG Kuanlin. An adversarial learning-based unsupervised domain adaptation method for semantic segmentation of high-resolution remote sensing images[J]. Remote Sensing for Natural Resources, 2024, 36(4): 149-157.
[14] ZHAO Jinling, HUANG Jian, LIANG Zijun, ZHAO Xuedan, JIN Tao, GE Hanghang, WEI Xiaoyan, SHAO Yuanzheng. BDANet-based assessment of building damage from earthquake disasters[J]. Remote Sensing for Natural Resources, 2024, 36(4): 193-200.
[15] GUO Yong, ZHANG Linxiang, XU Zeyu, CAI Zhongxiang. Remote sensing-based detection of dams in the Daqing River basin through optimization using hard negative samples of bridges[J]. Remote Sensing for Natural Resources, 2024, 36(4): 201-209.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech