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Found 18 papers, 8 papers with code
Mission Critical -- Satellite Data is a Distinct Modality in Machine Learning
Satellite data has the potential to inspire a seismic shift for machine learning -- one in which we rethink existing practices designed for traditional data modalities.
SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery
The resulting SatCLIP location encoder efficiently summarizes the characteristics of any given location for convenient use in downstream tasks.
Geographic Location Encoding with Spherical Harmonics and Sinusoidal Representation Networks
At the same time, little attention has been paid to the exact design of the neural network architectures with which these functional embeddings are combined.
Reflections from the Workshop on AI-Assisted Decision Making for Conservation
In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Research on Computation and Society at Harvard University on October 20-21, 2022.
Proceedings of the NeurIPS 2021 Workshop on Machine Learning for the Developing World: Global Challenges
These are the proceedings of the 5th workshop on Machine Learning for the Developing World (ML4D), held as part of the Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS) on December 14th, 2021.
GeoPointGAN: Synthetic Spatial Data with Local Label Differential Privacy
We introduce GeoPointGAN, a novel GAN-based solution for generating synthetic spatial point datasets with high utility and strong individual level privacy guarantees.
ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery
The potential for impact and scale of leveraging advancements in machine learning and remote sensing technologies is promising but needs to be of high quality in order to replace the current forest stock protocols for certifications.
Positional Encoder Graph Neural Networks for Geographic Data
Graph neural networks (GNNs) provide a powerful and scalable solution for modeling continuous spatial data.
Deployment Optimization for Shared e-Mobility Systems with Multi-agent Deep Neural Search
Shared e-mobility services have been widely tested and piloted in cities across the globe, and already woven into the fabric of modern urban planning.
SPATE-GAN: Improved Generative Modeling of Dynamic Spatio-Temporal Patterns with an Autoregressive Embedding Loss
In this study, we propose a novel loss objective combined with COT-GAN based on an autoregressive embedding to reinforce the learning of spatio-temporal dynamics.
Tackling the Overestimation of Forest Carbon with Deep Learning and Aerial Imagery
This proposal paper describes the first systematic comparison of forest carbon estimation from aerial imagery, satellite imagery, and ground-truth field measurements via deep learning-based algorithms for a tropical reforestation project.
Generative modeling of spatio-temporal weather patterns with extreme event conditioning
Deep generative models are increasingly used to gain insights in the geospatial data domain, e. g., for climate data.
Proceedings of the NeurIPS 2020 Workshop on Machine Learning for the Developing World: Improving Resilience
These are the proceedings of the 4th workshop on Machine Learning for the Developing World (ML4D), held as part of the Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS) on Saturday, December 12th 2020.
Population Mapping in Informal Settlements with High-Resolution Satellite Imagery and Equitable Ground-Truth
We propose a generalizable framework for the population estimation of dense, informal settlements in low-income urban areas--so called 'slums'--using high-resolution satellite imagery.
Auxiliary-task learning for geographic data with autoregressive embeddings
In this study, we propose SXL, a method for embedding information on the autoregressive nature of spatial data directly into the learning process using auxiliary tasks.
Augmenting correlation structures in spatial data using deep generative models
We empirically show the superiority of this approach over conventional ensemble learning approaches and rivaling spatial data augmentation methods, using synthetic and real-world prediction tasks.
Demand Prediction for Electric Vehicle Sharing
Electric Vehicle (EV) sharing systems have recently experienced unprecedented growth across the globe.
Community structures, interactions and dynamics in London's bicycle sharing network
Bikesharing schemes are transportation systems that not only provide an efficient mode of transportation in congested urban areas, but also improve last-mile connectivity with public transportation and local accessibility.
Social and Information Networks Computers and Society Physics and Society
