ArcGIS-based protocol to calculate the area fraction of landslide for multiple catchments

Yoshihara, Naoyuki

doi:10.1016/j.mex.2023.102064

Cited by 3 publications

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These images were used as samples to provide the visual information needed to train the deep learning model. The ArcGIS Pro platform [88] was used to train the model.…”

Section: Trainingmentioning

confidence: 99%

Sentinel-1 SAR Images and Deep Learning for Water Body Mapping

Pech-May

Aquino-Santos²,

Delgadillo-Partida³

2023

Remote Sensing

View full text Add to dashboard Cite

Floods occur throughout the world and are becoming increasingly frequent and dangerous. This is due to different factors, among which climate change and land use stand out. In Mexico, they occur every year in different areas. Tabasco is a periodically flooded region, causing losses and negative consequences for the rural, urban, livestock, agricultural, and service industries. Consequently, it is necessary to create strategies to intervene effectively in the affected areas. Different strategies and techniques have been developed to mitigate the damage caused by this phenomenon. Satellite programs provide a large amount of data on the Earth’s surface and geospatial information processing tools useful for environmental and forest monitoring, climate change impacts, risk analysis, and natural disasters. This paper presents a strategy for the classification of flooded areas using satellite images obtained from synthetic aperture radar, as well as the U-Net neural network and ArcGIS platform. The study area is located in Los Rios, a region of Tabasco, Mexico. The results show that U-Net performs well despite the limited number of training samples. As the training data and epochs increase, its precision increases.

show abstract

“…These images were used as samples to provide the visual information needed to train the deep learning model. The ArcGIS Pro platform [88] was used to train the model.…”

Section: Trainingmentioning

confidence: 99%