OPPORTUNITIES FOR MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE IN NATIONAL MAPPING AGENCIES: ENHANCING ORDNANCE SURVEY WORKFLOW

We identify 119 km² of meltwater area over West Antarctica in January 2017. In combination with Stokes et al., 2019, this forms the first continent-wide assessment helping to quantify the mass balance of Antarctica and its contribution to global sea level rise. We apply thresholds for meltwater classification to satellite images, mapping the extent and manually post-processing to remove false positives. Our study provides a high-fidelity dataset to train and validate machine learning methods.

The environmental effects of the world's largest Three Gorges Dam (TGD) have been received worldwide attentions. It is the first dynamics monitoring of mid-channel bars (MCBs) in the downstream of TGD at large spatiotemporal scale. The study shows most of the MCBs are located in the lower reach. Small MCBs become shorter and wider whereas the others became slimmer in post-TGD period. Impacts from the TGD operation on the MCB dynamics could decrease along with the increasing distance from TGD.

This research reveals new insights into the weather drivers of land surface phenology (LSP) across the entire European forest, while at the same time it establishes a new conceptual framework for modelling LSP. Specifically, a sophisticated machine learning regression method (RF) was introduced for LSP modelling across very large areas and across multiple years simultaneously. The RF models explained 81 and 62 % of the variance in the spring and autumn LSP interannual variation.