Earth Science Information Partners (ESIP)

Mentors: Student: Aarif Shaikh

<p>The objective of the proposal is twofold:- Build a Python library based on the workflow paradigm which would facilitate stream watershed delineation which would provide the segmented catchment basins given a coordinate reference system by the end user. Benchmark the performance of following such an approach and assess the suitability over traditional and prevalent HPC techniques</p>

Mentors: Student: Evandro C Taquary

<p>The increasing number of sensors orbiting the earth is systematically producing larger volumes of data, with better spatiotemporal resolutions. To deal with that, better accurate machine learning approaches, such as Deep Learning (DL), are needed to transform raw data into applicable Information. Several DL architectures (e.g. CNN, semantic segmentation) rely only at spatial dimension to perform, for example, land-cover/land-use (LCLU) maps, disregarding the temporal dependencies between pixels observations over the time. Also, high-res remote sensing data (e.g. Planet, Sentinel) may provide more consistent time-series, that can be use in the identification of important LCLU classes, like crop, pastureland and grasslands.</p> <p>This potential can be explored using Recurrent Neural Networks (RNN), a specific family of DL approaches which can take into account time dimension. A promising project idea would be implement a RNN approach (e.g. LSTM) to classify a Sentinel time-series that is able to produce a map for one or more LCLU classes and ship the final version of the model into the Pycoal library as part of the COAL project.</p>