Radiant Earth’s first online course aims to strengthen practitioners’ capacity and skills to create impactful machine learning applications.
Nature article: Earth Observation Epidemiology or tele-epidemiology is defined as ‘using space technology with remote sensing in epidemiology. It is a useful tool that is increasingly being used by clinicians and stakeholders for zoonotic infections1,2,4. Tele-epidemiology helped map out the spread of the Ebola virus among animals and can be used for risk mapping, risk communication and identifying vulnerable populations. Similarly, geographic information science technology can improve the understanding and control of COVID-19 through surveillance, data sharing, digital contact tracing and investigation of risk factors and infectious disease forecasting
Countries are using forests to pad their climate commitments. New satellite images might call their bluff
This Special Issue covers a broad range of topics, such as transfer learning, design of new Deep Neural Network (DNN), CNN, and GAN models, as well as a wide range of applications (Table 1), including agriculture (four papers), natural resources (three papers), marine environments (two papers), change detection (one paper), and disaster damage detection (one paper).
ML4EO Training given by Radiant Earth. Designed to strengthen practitioners’ local capacity and skills in support of creating impactful machine learning applications
The SpatioTemporal Asset Catalog (STAC) community is pleased to announce the release of version 1.0.0
The rapid advance of digital technologies has significantly impacted education in recent years. It is evident that the increasing digitization of the economy and society will require students to become comfortable with technology to prepare for the future. In turn, this also requires teachers to be supported to develop the skills and knowledge required to fully utilize the capabilities of technology, whether in the classroom or in a hybridized model that utilizes distributed online learning
Hatfield’s satellite deep learning project to monitor the habitat of Canada’s Woodland Caribou is featured by the Canadian Space Agency as part of their Biodiversity Day spotlight.
Technological advances happen quick and now with cloud infrastructures we have the unprecedented means to make such deep integration possible. However, transforming an established operational setup, such as was developed and used for the Global Land Service over the years, to another completely new and technological challenging cloud computing environment is not a trivial job. Especially considering that many production chains need to be decomposed into modular bits and pieces which then have to be newly forged into a smooth and fully integrated machinery to provide the user with a transparent, yet integrated, set of tools. The scope of this report is to tackle exactly this: providing clear suggestions for an efficient ‘cloudification’ of the Copernicus global land production lines and user interfaces, and investigating if there is a tangible benefit and what would be the effort involved.
The core tools of science (data, software, and computers) are undergoing a rapid and historic evolution, changing what questions scientists ask and how they find answers. Earth science data are being transformed into new formats optimized for cloud storage that enable rapid analysis of multi-petabyte datasets. Datasets are moving from archive centers to vast cloud data storage, adjacent to massive server farms. Open source cloud-based data science platforms, accessed through a web-browser window, are enabling advanced, collaborative, interdisciplinary science to be performed wherever scientists can connect to the internet. Specialized software and hardware for machine learning and artificial intelligence (AI/ML) are being integrated into data science platforms, making them more accessible to average scientists. Increasing amounts of data and computational power in the cloud are unlocking new approaches for data-driven discovery. For the first time, it is truly feasible for scientists to bring their analysis to data in the cloud without specialized cloud computing knowledge. This shift in paradigm has the potential to lower the threshold for entry, expand the science community, and increase opportunities for collaboration while promoting scientific innovation, transparency, and reproducibility. Yet, we have all witnessed promising new tools which seem harmless and beneficial at the outset become damaging or limiting. What do we need to consider as this new way of doing science is evolving?