TY  - JOUR
A1  - Schönfeldt, Elisabeth
A1  - Winocur, Diego
A1  - Pánek, Tomáš
A1  - Korup, Oliver
T1  - Deep learning reveals one of Earth's largest landslide terrain in Patagonia
JF  - Earth & planetary science letters
N2  - Hundreds of basaltic plateau margins east of the Patagonian Cordillera are undermined by numerous giant slope failures. However, the overall extent of this widespread type of plateau collapse remains unknown and incompletely captured in local maps. To detect giant slope failures consistently throughout the region, we train two convolutional neural networks (CNNs), AlexNet and U-Net, with Sentinel-2 optical data and TanDEM-X topographic data on elevation, surface roughness, and curvature. We validated the performance of these CNNs with independent testing data and found that AlexNet performed better when learned on topographic data, and UNet when learned on optical data. AlexNet predicts a total landslide area of 12,000 km2 in a study area of 450,000 km2, and thus one of Earth's largest clusters of giant landslides. These are mostly lateral spreads and rotational failures in effusive rocks, particularly eroding the margins of basaltic plateaus; some giant landslides occurred along shores of former glacial lakes, but are least prevalent in Quaternary sedimentary rocks. Given the roughly comparable topographic, climatic, and seismic conditions in our study area, we infer that basalts topping weak sedimentary rocks may have elevated potential for large-scale slope failure. Judging from the many newly detected and previously unknown landslides, we conclude that CNNs can be a valuable tool to detect large-scale slope instability at the regional scale. However, visual inspection is still necessary to validate results and correctly outline individual landslide source and deposit areas.
KW  - landslide detection
KW  - convolutional neural network
KW  - Patagonia
Y1  - 2022
U6  - https://doi.org/10.1016/j.epsl.2022.117642
SN  - 0012-821X
SN  - 1385-013X
VL  - 593
PB  - Elsevier
CY  - Amsterdam [u.a.]
ER  - 
TY  - JOUR
A1  - Seleem, Omar
A1  - Ayzel, Georgy
A1  - Costa Tomaz de Souza, Arthur
A1  - Bronstert, Axel
A1  - Heistermann, Maik
T1  - Towards urban flood susceptibility mapping using data-driven models in Berlin, Germany
JF  - Geomatics, natural hazards and risk
N2  - Identifying urban pluvial flood-prone areas is necessary but the application of two-dimensional hydrodynamic models is limited to small areas. Data-driven models have been showing their ability to map flood susceptibility but their application in urban pluvial flooding is still rare. A flood inventory (4333 flooded locations) and 11 factors which potentially indicate an increased hazard for pluvial flooding were used to implement convolutional neural network (CNN), artificial neural network (ANN), random forest (RF) and support vector machine (SVM) to: (1) Map flood susceptibility in Berlin at 30, 10, 5, and 2 m spatial resolutions. (2) Evaluate the trained models' transferability in space. (3) Estimate the most useful factors for flood susceptibility mapping. The models' performance was validated using the Kappa, and the area under the receiver operating characteristic curve (AUC). The results indicated that all models perform very well (minimum AUC = 0.87 for the testing dataset). The RF models outperformed all other models at all spatial resolutions and the RF model at 2 m spatial resolution was superior for the present flood inventory and predictor variables. The majority of the models had a moderate performance for predictions outside the training area based on Kappa evaluation (minimum AUC = 0.8). Aspect and altitude were the most influencing factors on the image-based and point-based models respectively. Data-driven models can be a reliable tool for urban pluvial flood susceptibility mapping wherever a reliable flood inventory is available.
KW  - Urban pluvial flood susceptibility
KW  - convolutional neural network
KW  - deep
KW  - learning
KW  - random forest
KW  - support vector machine
KW  - spatial resolution;
KW  - flood predictors
Y1  - 2022
U6  - https://doi.org/10.1080/19475705.2022.2097131
SN  - 1947-5705
SN  - 1947-5713
VL  - 13
IS  - 1
SP  - 1640
EP  - 1662
PB  - Taylor & Francis
CY  - London
ER  -