no code implementations • 2 Feb 2024 • Esther Rolf, Konstantin Klemmer, Caleb Robinson, Hannah Kerner
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.
1 code implementation • 28 Nov 2023 • Konstantin Klemmer, Esther Rolf, Caleb Robinson, Lester Mackey, Marc Rußwurm
The resulting SatCLIP location encoder efficiently summarizes the characteristics of any given location for convenient use in downstream tasks.
1 code implementation • 10 Oct 2023 • Marc Rußwurm, Konstantin Klemmer, Esther Rolf, Robin Zbinden, Devis Tuia
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.
no code implementations • 17 Jul 2023 • Lily Xu, Esther Rolf, Sara Beery, Joseph R. Bennett, Tanya Berger-Wolf, Tanya Birch, Elizabeth Bondi-Kelly, Justin Brashares, Melissa Chapman, Anthony Corso, Andrew Davies, Nikhil Garg, Angela Gaylard, Robert Heilmayr, Hannah Kerner, Konstantin Klemmer, Vipin Kumar, Lester Mackey, Claire Monteleoni, Paul Moorcroft, Jonathan Palmer, Andrew Perrault, David Thau, Milind Tambe
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.
no code implementations • 10 Jan 2023 • Paula Rodriguez Diaz, Tejumade Afonja, Konstantin Klemmer, Aya Salama, Niveditha Kalavakonda, Oluwafemi Azeez, Simone Fobi
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.
1 code implementation • 18 May 2022 • Teddy Cunningham, Konstantin Klemmer, Hongkai Wen, Hakan Ferhatosmanoglu
We introduce GeoPointGAN, a novel GAN-based solution for generating synthetic spatial point datasets with high utility and strong individual level privacy guarantees.
no code implementations • 26 Jan 2022 • Gyri Reiersen, David Dao, Björn Lütjens, Konstantin Klemmer, Kenza Amara, Attila Steinegger, Ce Zhang, Xiaoxiang Zhu
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.
1 code implementation • 19 Nov 2021 • Konstantin Klemmer, Nathan Safir, Daniel B. Neill
Graph neural networks (GNNs) provide a powerful and scalable solution for modeling continuous spatial data.
no code implementations • 3 Nov 2021 • Man Luo, Bowen Du, Konstantin Klemmer, HongMing Zhu, Hongkai Wen
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.
1 code implementation • 30 Sep 2021 • Konstantin Klemmer, Tianlin Xu, Beatrice Acciaio, Daniel B. Neill
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.
no code implementations • 23 Jul 2021 • Gyri Reiersen, David Dao, Björn Lütjens, Konstantin Klemmer, Xiaoxiang Zhu, Ce Zhang
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.
no code implementations • 26 Apr 2021 • Konstantin Klemmer, Sudipan Saha, Matthias Kahl, Tianlin Xu, Xiao Xiang Zhu
Deep generative models are increasingly used to gain insights in the geospatial data domain, e. g., for climate data.
no code implementations • 12 Jan 2021 • Tejumade Afonja, Konstantin Klemmer, Aya Salama, Paula Rodriguez Diaz, Niveditha Kalavakonda, Oluwafemi Azeez
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.
no code implementations • 17 Sep 2020 • Konstantin Klemmer, Godwin Yeboah, João Porto de Albuquerque, Stephen A Jarvis
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.
1 code implementation • 18 Jun 2020 • Konstantin Klemmer, Daniel B. Neill
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.
1 code implementation • 23 May 2019 • Konstantin Klemmer, Adriano Koshiyama, Sebastian Flennerhag
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.
no code implementations • 10 Mar 2019 • Man Luo, Hongkai Wen, Yi Luo, Bowen Du, Konstantin Klemmer, Hong-Ming Zhu
Electric Vehicle (EV) sharing systems have recently experienced unprecedented growth across the globe.
1 code implementation • 16 Apr 2018 • Fernando Munoz-Mendez, Konstantin Klemmer, Ke Han, Stephen Jarvis
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