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Found 8 papers, 2 papers with code
Assessing the Potential of AI for Spatially Sensitive Nature-Related Financial Risks
The two use cases also cover different sectors, geographies, financial assets and AI modelling techniques, providing an overview on how AI could be applied to different challenges relating to nature's integration into finance.
Disaster mapping from satellites: damage detection with crowdsourced point labels
High-resolution satellite imagery available immediately after disaster events is crucial for response planning as it facilitates broad situational awareness of critical infrastructure status such as building damage, flooding, and obstructions to access routes.
Mining and Tailings Dam Detection In Satellite Imagery Using Deep Learning
This work explores the combination of free cloud computing, free open-source software, and deep learning methods to analyse a real, large-scale problem: the automatic country-wide identification and classification of surface mines and mining tailings dams in Brazil.
Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sources
Such applications depend on classifying the situation across a region of interest, which can be depicted as a spatial "heatmap".
BCCNet: Bayesian classifier combination neural network
Machine learning research for developing countries can demonstrate clear sustainable impact by delivering actionable and timely information to in-country government organisations (GOs) and NGOs in response to their critical information requirements.
Anomaly Detection and Removal Using Non-Stationary Gaussian Processes
This paper proposes a novel Gaussian process approach to fault removal in time-series data.
Automated Machine Learning on Big Data using Stochastic Algorithm Tuning
However, the state of the art in Bayesian optimisation is incapable of scaling to the large number of evaluations of algorithm performance required to fit realistic models to complex, big data.
Efficient State-Space Inference of Periodic Latent Force Models
We apply our approach to model the thermal dynamics of domestic buildings and show that it is effective at predicting day-ahead temperatures within the homes.
