The Geo-ICT Blog

Tutorials/Post - Remote Sensing, GIS, Earth System, Geo-AI/ML

Surface Water Detected by the BCE Algorithm

Deep Learning approach for Sentinel-1 Surface Water Mapping leveraging Google Earth Engine

Satellite remote sensing plays an important role in mapping the location and extent of surface water. A variety of approaches are available for mapping surface water, but deep learning approaches are not commonplace as they are ’data hungry’ and require large amounts of computational resources. However, with the availability of various satellite sensors and rapid … CONTINUE READING

Mapping sugarcane in Thailand using transfer learning, a lightweight convolutional neural network, NICFI high resolution satellite imagery and Google Earth Engine

Air pollution from burning sugarcane is an important environmental issue in Thailand. Knowing the location and extent of sugarcane plantations would help in formulating effective strategies to reduce burning. High-resolution satellite imagery combined with deep-learning technologies can be effective to map sugarcane with high precision. However, land cover mapping using high-resolution data and computationally intensive … CONTINUE READING

Screenshot of loading custom base map module.

Adding custom basemaps to Google Earth Engine code editor

I happened across an interesting Github repository from Samapriya Roy the other day for creating custom basemaps to add to your Google Earth Engine map. Traditionally when using or sharing your work in GEE you have the option between a standard Google cartographic basemap or the ‘satellite’ view ( in quotations here since it’s my … CONTINUE READING

Automatic Detection of Impervious Surfaces from Remotely Sensed Data Using Deep Learning

The large-scale quantification of impervious surfaces provides valuable information for urban planning and socioeconomic development. Remote sensing and GIS techniques provide spatial and temporal information of land surfaces and are widely used for modeling impervious surfaces. Traditionally, these surfaces are predicted by computing statistical indices derived from different bands available in remotely sensed data, such … CONTINUE READING

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