18 citations · 68 across the 16 of their papers we have counts for
16 papers
Reflections from the Workshop on AI-Assisted Decision Making for Conservation
Lily Xu, Esther Rolf, Sara Beery +21
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 Res…
Proceedings of the NeurIPS 2021 Workshop on Machine Learning for the Developing World: Global Challenges
Paula Rodriguez Diaz, Tejumade Afonja, Konstantin Klemmer +4
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 Syst…
GeoPointGAN: Synthetic Spatial Data with Local Label Differential Privacy
Teddy Cunningham, Konstantin Klemmer, Hongkai Wen +1
Synthetic data generation is a fundamental task for many data management and data science applications. Spatial data is of particular interest, and its sensitive nature often leads…
ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery
Gyri Reiersen, David Dao, Björn Lütjens +5
Forest biomass is a key influence for future climate, and the world urgently needs highly scalable financing schemes, such as carbon offsetting certifications, to protect and resto…
Deployment Optimization for Shared e-Mobility Systems with Multi-agent Deep Neural Search
Man Luo, Bowen Du, Konstantin Klemmer +2
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. This paper studies a practica…
Positional Encoder Graph Neural Networks for Geographic Data
Konstantin Klemmer, Nathan Safir, Daniel B. Neill
Graph neural networks (GNNs) provide a powerful and scalable solution for modeling continuous spatial data. However, they often rely on Euclidean distances to construct the input g…
SPATE-GAN: Improved Generative Modeling of Dynamic Spatio-Temporal Patterns with an Autoregressive Embedding Loss
Konstantin Klemmer, Tianlin Xu, Beatrice Acciaio +1
From ecology to atmospheric sciences, many academic disciplines deal with data characterized by intricate spatio-temporal complexities, the modeling of which often requires special…
Tackling the Overestimation of Forest Carbon with Deep Learning and Aerial Imagery
Gyri Reiersen, David Dao, Björn Lütjens +3
Forest carbon offsets are increasingly popular and can play a significant role in financing climate mitigation, forest conservation, and reforestation. Measuring how much carbon is…
Generative modeling of spatio-temporal weather patterns with extreme event conditioning
Konstantin Klemmer, Sudipan Saha, Matthias Kahl +2
Deep generative models are increasingly used to gain insights in the geospatial data domain, e.g., for climate data. However, most existing approaches work with temporal snapshots…
Proceedings of the NeurIPS 2020 Workshop on Machine Learning for the Developing World: Improving Resilience
Tejumade Afonja, Konstantin Klemmer, Aya Salama +3
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 Sys…
Population Mapping in Informal Settlements with High-Resolution Satellite Imagery and Equitable Ground-Truth
Konstantin Klemmer, Godwin Yeboah, João Porto de Albuquerque +1
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 imag…
Auxiliary-task learning for geographic data with autoregressive embeddings
Konstantin Klemmer, Daniel B. Neill
Machine learning is gaining popularity in a broad range of areas working with geographic data, such as ecology or atmospheric sciences. Here, data often exhibit spatial effects, wh…
Augmenting correlation structures in spatial data using deep generative models
Konstantin Klemmer, Adriano Koshiyama, Sebastian Flennerhag
State-of-the-art deep learning methods have shown a remarkable capacity to model complex data domains, but struggle with geospatial data. In this paper, we introduce SpaceGAN, a no…
Demand Prediction for Electric Vehicle Sharing
Man Luo, Hongkai Wen, Yi Luo +3
Electric Vehicle (EV) sharing systems have recently experienced unprecedented growth across the globe. Many car sharing service providers as well as automobile manufacturers are en…
Modeling Rape Reporting Delays Using Spatial, Temporal and Social Features
Konstantin Klemmer, Daniel B. Neill, Stephen A. Jarvis
We present a novel approach to estimate the delay observed between the occurrence and reporting of rape crimes. We explore spatial, temporal and social effects in sparse aggregated…
Community Structures, Interactions and Dynamics in London's Bicycle Sharing Network
Fernando Munoz-Mendez, Konstantin Klemmer, Ke Han +1
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 publ…