Machine Learning For Remote Sensing Based Landcover Change Detection


Remote sensing (RS) datasets have near-global coverage and can be used to assess the distribution of different land cover and changes in their distribution at different spatial-temporal scales. Being able to assess land cover changes (and their impacts) is vital for both conservationists and those from geology and site remediation related industries. By completing this workshop, you will develop an understanding of the different freely available RS data sources out there and open-source software tools that can be used for analysing these. We will cover:
Session 1: Overview of freely available RS data sources and how open-source software tools such as Python, Google Earth Engine, QGIS and R can be used for developing spatial data analysis workflows
Session 2: Basic spatial data analysis preprocessing and visualisations
Session 3: Introduction to machine learning. Carry out practical ML-based spatial data analysis using open source tools. Formal model assessment and change detection analysis

Background Knowledge
(1) Prior exposure to basic spatial data and machine learning concepts (e.g. difference between rasters and vectors and unsupervised vs supervised classification)
(2) Prior exposure to either R or Python programming languages
(3) Most important: An interest in applying machine learning algorithms to spatial data problems


I joined the Center for Environmental Policy (CEP), Imperial College London as a Research Fellow in 2018. Before that, I completed a PhD from the University of Cambridge in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have nearly 10 year's experience in conducting academic research at the interface of tropical ecology, data science, earth observation (EO) and artificial intelligence (AI)and published 14 first author peer-reviewed papers in international journals since 2013 including PLoS One. I have obtained research funding of approximately £275,000 since 2018 and provided research, consultancy and data science support to startups and industry partners including Treeconomy and Morphobotics LLC. I am a Fellow of the Royal Geographic Society (RGS)and Royal Statistical Society (RSS) as well as serving as an Associate Fellow at the Data Science Institute, Imperial College London. I am also a best selling course-instructor on the online MOOC platform Udemy where I provide online teaching to more than 71,000 students from across the world on machine learning, earth observation and deep learning related topic

Open Data Science




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