Nature-based carbon sequestration is currently the most viable solutions to extract CO2from the atmosphere and convert it into carbon. Oceans, soils and forests have the potential to capture and store large amount of carbon for decades. There is an ongoing debate about the permanence of the carbon sequestered by nature-based processes and the precise techniques required to monitor these carbon pools. Remote sensing plays a crucial role in the large scale observations of the Earth surface and provides a scalable method to monitor land use that can affect carbon sequestration. Optical spectral information and radar signals are the best candidates as proxy data to quantify and monitor the change in carbon sequestered. Here we outline the design of an AI enabled framework to monitor, verify, and quantify carbon sequestration in nature-based carbon sequestration processes.

A robust Origin-Destination (OD) prediction is key to urban mobility. A good forecasting model can reduce operational risks and improve service availability, among many other upsides. Here, we examine the use of Graph Convolutional Net-work (GCN) and its hybrid Markov-Chain (GCN-MC) variant to perform a context-aware OD prediction based on a large-scale public transportation dataset in Singapore. Compared with the baseline Markov-Chain algorithm and GCN, the proposed hybrid GCN-MC model improves the prediction accuracy by 37% and 12% respectively. Lastly, the addition of temporal and historical contextual information further improves the performance of the proposed hybrid model by 4 –12%.