Landslides Information Extraction Using Object-Oriented Image Analysis Paradigm Based on Deep Learning and Transfer Learning

Heng Lü, Lei Ma, Xiao Fu, Chao Liu, Zhi Wang, Min Tang, Naiwen Li

Remote Sensing 2020 article cited by 89

Source Open-access PDF More in Climate & Environment

importance 6/10 ai assisted Landslides and related hazards disaster response stage: pilot

Problem

Quickly and accurately extracting landslide disaster information from UAV remote sensing images is hard because spectral information is deficient and manual interpretation is too slow for emergencies.

Why it matters

Fast landslide mapping is critical for disaster prevention and emergency relief response.

What the AI did

Deep learning and transfer learning were applied within an object-oriented image analysis pipeline on pre-processed UAV orthoimages of landslide-prone areas in Sichuan, China.

Results

not stated in source

Impact

Applied to UAV imagery of high-occurrence landslide areas in Wenchuan and Baoxing counties per the abstract.

Record

Venue	Remote Sensing
Published	2020
Work type	article
Authors	Heng Lü, Lei Ma, Xiao Fu, Chao Liu, Zhi Wang, Min Tang, Naiwen Li
Citations	89 (OpenAlex, at import time)
References	54
Model or method	deep learning and transfer learning (specific architecture not stated in source)
Organization	not stated in source
Primary source	https://doi.org/10.3390/rs12050752
Open access	gold
Keywords	landslides, UAV remote sensing, deep learning, transfer learning, image segmentation, disaster response

Importance rating

6 / 10

significant — thousands to millions affected

Fast landslide mapping is critical for disaster prevention and emergency relief, affecting lives; demonstrated on UAV imagery of two landslide-prone counties, no numeric scale given.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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