Dépôt numérique

Landslide detection using deep learning and object-based image analysis.

Ghorbanzadeh, Omid, Shahabi, Hejar, Crivellari, Alessandro, Homayouni, Saeid ORCID: https://orcid.org/0000-0002-0214-5356, Blaschke, Thomas et Ghamisi, Pedram (2022). Landslide detection using deep learning and object-based image analysis. Landslides , vol. 19 , nº 4. p. 929-939. DOI: 10.1007/s10346-021-01843-x.

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Recent landslide detection studies have focused on pixel-based deep learning (DL) approaches. In contrast, intuitive annotation of landslides from satellite imagery is based on distinct features rather than individual pixels. This study examines the feasibility of the integration framework of a DL model with rule-based object-based image analysis (OBIA) to detect landslides. First, we designed a ResU-Net model and then trained and tested it in the Sentinel-2 imagery. Then we developed a simple rule-based OBIA with only four rulesets, applying it first to the original image dataset and then to the same dataset plus the resulting ResU-Net heatmap. The value of each pixel in the heatmap refers to the probability that the pixel belongs to either landslide or non-landslide classes. Thus, we evaluate three scenarios: ResU-Net, OBIA, and ResU-Net-OBIA. The landslide detection maps from three different classification scenarios were compared against a manual landslide inventory map using thematic accuracy assessment metrics: precision, recall, and f1-score. Our experiments in the testing area showed that the proposed integration framework yields f1-score values 8 and 22 percentage points higher than those of the ResU-Net and OBIA approaches, respectively.

Type de document: Article
Mots-clés libres: convolutional neural network (CNN); deep learning (DL); fully convolutional network (FCN); object-based image analysis (OBIA); optical satellite imagery; rapid mapping; ResU-Net
Centre: Centre Eau Terre Environnement
Date de dépôt: 23 juin 2022 14:56
Dernière modification: 23 juin 2022 14:56
URI: https://espace.inrs.ca/id/eprint/12678

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