Land cover change and its impact on food security is a topic that has major implications for development in population-dense Southeast Asia. The main drivers of forest loss include the expansion of agriculture and plantation estates, growth of urban centers,extraction of natural resources, and water infrastructure development. The design and implementation of appropriate land use … CONTINUE READING
Tag: Machine Learning
Land cover maps are a critical component to make informed policy, development, planning, and resource management decisions. However, technical, capacity and institutional challenges inhibit the creation of consistent and relevant land cover maps for use in developing regions. Many developing regions lack coordinated capacity, infrastructure, and technologies to produce a robust land cover monitoring system … CONTINUE READING
Land cover maps play an integral role in environmental management. However, countries and institutes encounter many challenges with producing timely, efficient, and temporally harmonized updates to their land cover maps. To address these issues we present a modular Regional Land Cover Monitoring System (RLCMS) architecture that is easily customized to create land cover products using … CONTINUE READING
With the recent release of TensorFlow 2.0, showcased at GEO for Good 2019, there is increased interest in employing an array of neural net approaches to solve various Remote Sensing research questions. A dedicated group from NASA SERVIR has spent the last 5 days exploring the Google Earth Engine, Google Colaboratory, and Google … CONTINUE READING
This simple UI interface is built on the Google Earth Engine. It uses the primitives as percentage layers to assemble them to derive land cover maps. Here we are using the Random Forest algorithm to generate the land cover maps.
You can use the sliders to change the threshold input for the tree node structure … CONTINUE READING