&gt;Movimentos de massa na região de Caraguatatuba e São Sebastião (SP) entre 1967 a 2011: utilização de cicatrizes de escorregamento como subsídio a estudos de retro-análise

Correa, Claudia V.; Reis, Fábio Augusto Gomes Vieira; Giordano, Lucília Carmo; Cerri, Rodrigo Irineu; Chaves, Camila Jardinetti; Gabelini, Beatriz Marquez

doi:10.20396/sbgfa.v1i2017.2599

Cited by 1 publication

(1 citation statement)

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“…An inventory map is available for a major event that occurred in 1967, when almost 600 landslides of various sizes were identified, which caused destruction and human losses (Fúlfaro et al, 1976). Despite the high landslide occurrence rate in the region, there is a lack of landslide documentation, with few studies updating the previously inventory map and monitoring the landslides (Correa et al, 2017;Dias et al, 2021).…”

Section: Study Areamentioning

confidence: 99%

Relict landslide detection in rainforest areas using a combination of k-means clustering algorithm and Deep-Learning semantic segmentation models

Garcia¹,

Grohmann²,

Soares³

et al. 2022

Preprint

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Landslides are destructive and recurrent natural disasters on steep slopes and represent a risk to lives and properties. Knowledge of relict landslides' location is vital to understand their mechanisms, update inventory maps and improve risk assessment. However, relict landslide mapping is complex in tropical regions covered with rainforest vegetation. A new CNN approach is proposed for semi-automatic detection of relict landslides, which uses a dataset generated by a k-means clustering algorithm and has a pre-training step. The weights computed in the pre-training are used to fine-tune the CNN training process. A comparison between the proposed and standard approaches is performed using CBERS-4A WPM images. Three CNNs for semantic segmentation are used (U-Net, FPN, Linknet) with two augmented datasets. A total of 42 combinations of CNNs are tested. Values of precision and recall were very similar between the combinations tested. Recall was higher than 75% for every combination, but precision values were usually smaller than 20%. False positives (FP) samples were addressed as the cause for these low precision values. Predictions of the proposed approach were more accurate and correctly detected more landslides. This work demonstrates that there are limitations for detecting relict landslides in areas covered with rainforest, mainly related to similarities between the spectral response of pastures and deforested areas with Gleichenella sp. ferns, commonly used as an indicator of landslide scars.

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Section: Study Areamentioning

confidence: 99%