FLASH: Flexible and lightweight awareness of slope hazard

Lin CHEN; Qing ZHU; Han HU; Yulin DING; Pengxin GU

doi:10.7527/S1000-6893.2025.31293

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2025 , Vol. 46 >Issue 11: 531293 - 531293

DOI: https://doi.org/10.7527/S1000-6893.2025.31293

Articles

FLASH: Flexible and lightweight awareness of slope hazard

Lin CHEN ,
Qing ZHU ,
Han HU ,
Yulin DING ,
Pengxin GU

Expand

Faculty of Geosciences and Engineering，Southwest Jiaotong University，Chengdu 611756，China

E-mail： zhuqing@swjtu.edu.cn

Received date: 2024-09-30

Revised date: 2024-12-07

Accepted date: 2025-02-28

Online published: 2025-03-19

Supported by

National Natural Science Foundation of China(42230102)

Fold

Abstract

To address the urgent needs of emergency response for major landslide disasters under extreme conditions such as disconnected roads， power outages， and network failures， this study designs and develops FLASH， a lightweight UAV-based landslide perception system which integrates hardware and algorithms. The hardware system includes a lightweight perception module and real-time interpretation unit， designed as decoupled standalone nodes. Utilizing ROS-based messaging and serial communication for low-latency data transfer， the system operates at a peak power of 25 W and weighs 500 g. Algorithmically， a lightweight design model incorporating spatial-informational constraints is proposed， which maintains inference accuracy while drastically reducing latency for semantic segmentation of ultra-high-resolution images （e.g.， 78.88 ms per 7 952×5 304 image）. The model further enables intelligent multi-image landslide retrieval and volume estimation via deep semantic representation， achieving 81% reliability in volume calculations （validated in the Songpan landslide case） without relying on 3D reconstruction. Ultimately， this system realizes the fully automated extraction of critical disaster information from UAV imagery acquisition to landslide volume estimation， significantly enhancing the efficiency of landslide disaster interpretation.

Key words： real-time landslide extraction; UAV imagery; emergency response; edge device; system integration; lightweight design; ROS system

Cite this article

Lin CHEN , Qing ZHU , Han HU , Yulin DING , Pengxin GU . FLASH: Flexible and lightweight awareness of slope hazard[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2025 , 46(11) : 531293 -531293 . DOI: 10.7527/S1000-6893.2025.31293

References

	?［1］朱庆，曾浩炜，丁雨淋，等. 重大滑坡隐患分析方法综述?［J］. 测绘学报， 2019， 48（12）： 1551-1561.
	ZHU Q， ZENG H W， DING Y L， et al. A review of major potential landslide hazards analysis??［J］. Acta Geodaetica et Cartographica Sinica， 2019， 48（12）： 1551-1561 （in Chinese）.
	?［2］ NIYOKWIRINGIRWA P， LOMBARDO L， DEWITTE O， et al. Event-based rainfall-induced landslide inventories and rainfall thresholds for Malawi?［J］. Landslides， 2024， 21（6）： 1403-1424.
	?［3］ WANG X， FAN X M， XU Q， et al. Change detection-based co-seismic landslide mapping through extended morphological profiles and ensemble strategy?［J］. ISPRS Journal of Photogrammetry and Remote Sensing， 2022， 187： 225-239.
	?［4］ ZHOU M J， YUAN M Z， YANG G X， et al. Risk analysis of road networks under the influence of landslides by considering landslide susceptibility and road vulnerability： A case study?［J］. Natural Hazards Research， 2024， 4（3）： 387-400.
	?［5］ CHEN L， DING Y L， HU H， et al. Landslide extraction using fused local and nonlocal attentional features on edge device toward embedded UAV emergency response?［J］. IEEE Transactions on Geoscience and Remote Sensing， 2024， 62： 5625720.
	?［6］朱庆，曹振宇，林珲，等. 应急测绘保障体系若干关键问题研究?［J］. 武汉大学学报（信息科学版）， 2014， 39（5）： 551-555.
	ZHU Q， CAO Z Y， LIN H， et al. Key technologies of emergency surveying and mapping service system?［J］. Geomatics and Information Science of Wuhan University， 2014， 39（5）： 551-555 （in Chinese）.
	?［7］刘飞，朱庆，丁雨淋，等. 滑坡—堰塞湖灾情无人机应急测绘、分析与险情模拟?［J］. 山地学报， 2021， 39（4）： 600-610.
	LIU F， ZHU Q， DING Y L， et al. Analysis and simulation of landslide-barrier lake disaster based on UAV emergency mapping?［J］. Mountain Research， 2021， 39（4）： 600-610 （in Chinese）.
	?［8］ WAN Y T， ZHONG Y F， MA A L， et al. An accurate UAV 3-D path planning method for disaster emergency response based on an improved multiobjective swarm intelligence algorithm?［J］. IEEE Transactions on Cybernetics， 2023， 53（4）： 2658-2671.
	?［9］ ZHANG Y J， LI J， LIU J J， et al. Extraction of landslide morphology based on topographic profile along the direction of slope movement using UAV images?［J］. Geomatics， Natural Hazards and Risk， 2023， 14（1）： 2278276.
	?［10］ LIU P， WEI Y M， WANG Q J， et al. Research on post-earthquake landslide extraction algorithm based on improved U-Net model?［J］. Remote Sensing， 2020， 12（5）： 894.
	?［11］ YAO Z H， CHENG W C， ZHANG W， et al. The rise of UAV fleet technologies for emergency wireless communications in harsh environments?［J］. IEEE Network， 2022， 36（4）： 28-37.
	?［12］ LONG J， SHELHAMER E， DARRELL T. Fully convolutional networks for semantic segmentation?［C］∥2015 IEEE Conference on Computer Vision and Pattern Recognition（CVPR）. Piscataway： IEEE Press， 2015： 3431-3440.
	?［13］ BO W H， LIU J， FAN X J， et al. BASNet： Burned area segmentation network for real-time detection of damage maps in remote sensing images??［J］. IEEE Transactions on Geoscience and Remote Sensing， 2022， 60： 5627913.
	?［14］ LI M L， ZHAO X K， LI J S， et al. ComNet： Combinational neural network for object detection in UAV-borne thermal images?［J］. IEEE Transactions on Geoscience and Remote Sensing， 2020， 59（8）： 6662-6673.
	?［15］ SANDRIC I， CHITU Z， ILINCA V， et al. Using high-resolution UAV imagery and artificial intelligence to detect and map landslide cracks automatically?［J］. Landslides， 2024， 21（10）： 2535-2543.
	?［16］ DONG Z Y， AN S， ZHANG J， et al. L-unet： A landslide extraction model using multi-scale feature fusion and attention mechanism?［J］. Remote Sensing， 2022， 14（11）： 2552.
	?［17］ CHEN H S， HE Y， ZHANG L F， et al. A landslide extraction method of channel attention mechanism U-Net network based on Sentinel-2A remote sensing images?［J］. International Journal of Digital Earth， 2023， 16（1）： 552-577.
	?［18］ JI S P， YU D W， SHEN C Y， 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.
	?［19］ MACENSKI S， FOOTE T， GERKEY B， et al. Robot Operating System 2： Design， architecture， and uses in the wild?［J］. Science Robotics， 2022， 7（66）： eabm6074.
	?［20］ CHEN Y P， DAI X Y， CHEN D D， et al. Mobile-former： Bridging MobileNet and transformer?［C］∥2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2022.
	?［21］ MEHTA S， RASTEGARI M. MobileViT： Light-weight， general-purpose， and mobile-friendly vision transformer?［C］∥International Conference on Learning Representations， 2022.
	?［22］ DEHGHANI M， ARNAB A， BEYER L， et al. The efficiency misnomer?［DB/OL］. arXiv preprint： 2110.12894， 2022.
	?［23］ VASU P K A， GABRIEL J， ZHU J， et al. MobileOne： an improved one millisecond mobile backbone?［C］∥2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2023.
	?［24］ DING X H， ZHANG X Y， MA N N， et al. RepVGG： Making VGG-style ConvNets great again?［C］?∥2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2021.
	?［25］ Vaswani A. Attention is all you need?［C］∥Advances in Neural Information Processing Systems， 2017.
	?［26］ FU J， LIU J， TIAN H J， et al. Dual attention network for scene segmentation?［C］?∥2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2019.
	?［27］ KATHAROPOULOS A， VYAS A， PAPPAS N， et al. Transformers are RNNs： Fast autoregressive transformers with linear attention?［C］∥International conference on machine learning， 2020.
	?［28］ HAN D C， YE T Z， HAN Y Z， et al. Agent attention： On the integration of softmax and linear attention?［M］∥Computer Vision-ECCV 2024. Cham： Springer Nature Switzerland， 2024.
	?［29］ XIE E Z， WANG W H， YU Z D， et al. SegFormer： Simple and efficient design for semantic segmentation with transformers?［J］. Advances in Neural Information Processing Systems， 2021， 34： 12077-12090.
	?［30］ GUO M H， LU C Z， HOU Q， et al. Segnext： Rethinking convolutional attention design for semantic segmentation?［J］. Advances in Neural Information Processing Systems， 2022， 35： 1140-1156.
	?［31］方群生，唐川，程霄，等. 汶川震区泥石流流域内滑坡物源量计算方法探讨?［J］. 水利学报， 2015， 46（11）： 1298-1304.
	FANG Q S， TANG C， CHENG X， et al. An calculation method for predicting landslides volumes of the debris flows in the Wenchuan earthquake area?［J］. Journal of Hydraulic Engineering， 2015， 46（11）： 1298-1304 （in Chinese）.
	?［32］ RONNEBERGER O， FISCHER P， BROX T. U-Net： Convolutional networks for biomedical image segmentation?［M］?∥Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015. Cham： Springer International Publishing， 2015： 234-241.
	?［33］ CHEN L C， ZHU Y K， PAPANDREOU G， et al. Encoder-decoder with atrous separable convolution for semantic image segmentation?［C］∥Computer Vision-ECCV 2018. Cham： Springer International Publishing， 2018.
	?［34］ YAN S， WU C L， WANG L Z， et al. DDRNet： Depth map denoising and refinement for consumer depth cameras using cascaded CNNs?［C］∥Computer Vision-ECCV 2018. Cham： Springer International Publishing， 2018.
	?［35］ YU C Q， WANG J B， PENG C， et al. BiSeNet： Bilateral segmentation network for real-time semantic segmentation?［C］?∥Computer Vision-ECCV 2018. Cham： Springer International Publishing， 2018： 334-349.
	?［36］ LI X， ZHOU Y M， PAN Z， et al. Partial order pruning： For best speed/accuracy trade-off in neural architecture search?［C］∥2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2019.
	?［37］ LI X T， YOU A S， ZHU Z， et al. Semantic flow for fast and accurate scene parsing?［M］∥Computer Vision-ECCV 2020. Cham： Springer International Publishing， 2020： 775-793.

Options

Outlines

模态框（Modal）标题

Abstract

Cite this article

References