This presentation walks through the use of Machine Learning and Artificial Intelligence to detect marine debris using planet data. Thanks to Lillianne Thomas from Development Seed and Ankur Shah from Climate Engine for the presentation. Thanks to the entire project team, especially Muthukumaran Ramasubramanian from the University of Alabama in Huntsville, George Priftis from the ..read more

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 ..read more

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 ..read more

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 ..read more

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 ..read more

Purpose: This short tutorial focuses on understanding how to query and download the data from the Goddard Earth Sciences Data and Information Services Center (GES DISC). Since GES DISC consists of 32 projects and missions, we will look into one of the commonly used data – The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG ..read more

GDAL is a powerful geospatial processing library that backups many of the geospatial software you are using. The full list can be found here. In this post, we will look at a very basic example of reading the raster file including all the bands and looping through the bands to get the statistics (Minimum ..read more

Satellite remote sensing plays an important role in the monitoring of surface water for historical analysis and near-real-time applications. Due to its cloud penetrating capability, many studies have focused on providing efficient and high-quality methods for surface water mapping using Synthetic Aperture Radar (SAR). However, few studies have explored the effects of SAR pre-processing steps ..read more

In this tutorial, we will use the MODIS based V6 Terra Vegetation Indices 16-Day Global 250m product. This MODIS NDVI and EVI products are computed from atmospherically corrected bi-directional surface reflectances that have been masked for water, clouds, heavy aerosols, and cloud shadows. We will use this product to create NDVI based threshold classified thematic ..read more

Understanding land cover change dynamics and potential pathways of change is of critical importance for sustainable resource management, to promote food security and resilience on a range of spatial scales. Data scarcity is a key concern, however, with the availability of free Earth Observation (EO) data, such challenges can be suitably addressed. In this research ..read more