Satellite Derived Bathymetry Using Machine Learning and Multi-Temporal Satellite Images

Dust source mapping using satellite imagery and machine learning models

Predicting dust sources area and determining the affecting factors is necessary in order to prioritize management and practice deal with desertification due to wind erosion in arid areas. Therefore, this study aimed to evaluate the application of three machine learning models (including generalized linear model, artificial neural network, random forest) to predict the vulnerability of dust cent...

Multi Resolution Fuzzy Segmentation of Satellite Images

Evaluating machine learning methods and satellite images to estimate combined climatic indices

The reflections recorded on satellite images have been affected by various environmental factors. In these images, some of these factors are combined with other environmental factors that cannot be distinguished. Therefore, it seems wise to model these environmental phenomena in the form of hybrid indicators. In this regard, satellite imagery and machine learning methods can play a unique role ...

Analyzing Urban Sprawl Spatial Fragmentation Using Multi-temporal Satellite Images

The landscape pattern of Daqing City, China has undergone a significant change during the last two decades as a result of rapid urbanization. A quantitative analysis describing the spatial fragmentation caused by urban sprawl from satellite images was presented on three dates: 1979, 1990, and 2000. Five landscape indices with supplementary ecological meanings were chosen to facilitate our exami...

Using deep learning to pansharpen satellite images

Context: The latest generation of satellite-based imaging sensors (Pleiades, Sentinel, etc.) acquires big volumes of Earth’s images with high spatial, spectral and temporal resolution (up to 50cm/pixel, 50 bands, twice per day, covering the full planet!). These data open the door to a large range of important applications, such as the planning of urban environments and the monitoring of natural...