Kris Sankaran

Andrew Slavin Ross

Nikola Milojevic-Dupont

Natasha Jaques

Anna Waldman-Brown

Alexandra Luccioni

Tegan Maharaj

Evan David Sherwin

S. Karthik Mukkavilli

Konrad Paul Kording

Carla P. Gomes

Andrew Y. Ng

Demis Hassabis

John C. Platt

Felix Creutzig … (voir 2 de plus)

Jennifer T Chayes

Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here… (voir plus) we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.

2022-02-07

ACM Computing Surveys (publié)

doi.org

FloW: A Dataset and Benchmark for Floating Waste Detection in Inland Waters

Yuwei Cheng

Jiannan Zhu

Mengxin Jiang

Jie Fu

Changsong Pang

Peidong Wang

Olawale Moses Onabola

Yimin Liu

Dianbo Liu

Marine debris is severely threatening the marine lives and causing sustained pollution to the whole ecosystem. To prevent the wastes from ge… (voir plus)tting into the ocean, it is helpful to clean up the floating wastes in inland waters using the autonomous cleaning devices like unmanned surface vehicles. The cleaning efficiency relies on a high-accurate and robust object detection system. However, the small size of the target, the strong light reflection over water surface, and the reflection of other objects on bank-side all bring challenges to the vision-based object detection system. To promote the practical application for autonomous floating wastes cleaning, we present FloW†, the first dataset for floating waste detection in inland water areas. The dataset consists of an image sub-dataset FloW-Img and a multimodal sub-dataset FloW-RI which contains synchronized millimeter wave radar data and images. Accurate annotations for images and radar data are provided, supporting floating waste detection strategies based on image, radar data, and the fusion of two sensors. We perform several baseline experiments on our dataset, including vision-based and radar-based detection methods. The results show that, the detection accuracy is relatively low and floating waste detection still remains a challenging task.

2021-10-01

IEEE International Conference on Computer Vision (publié)

doi.org

Machine Learning for Glacier Monitoring in the Hindu Kush Himalaya

Shimaa Baraka

Benjamin Akera

Bibek Aryal

Tenzing Chogyal Sherpa

Finu Shresta

Anthony Ortiz

J. Ferres

M. Matin

2020-12-09

ArXiv (prépublication)

HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery

Michel Deudon

Alfredo Kalaitzis

Israel Goytom

Md Rifat Arefin

Zhichao Lin

Vincent Michalski

Samira Ebrahimi Kahou

Julien Cornebise

Generative deep learning has sparked a new wave of Super-Resolution (SR) algorithms that enhance single images with impressive aesthetic res… (voir plus)ults, albeit with imaginary details. Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views. This is important for satellite monitoring of human impact on the planet -- from deforestation, to human rights violations -- that depend on reliable imagery. To this end, we present HighRes-net, the first deep learning approach to MFSR that learns its sub-tasks in an end-to-end fashion: (i) co-registration, (ii) fusion, (iii) up-sampling, and (iv) registration-at-the-loss. Co-registration of low-resolution views is learned implicitly through a reference-frame channel, with no explicit registration mechanism. We learn a global fusion operator that is applied recursively on an arbitrary number of low-resolution pairs. We introduce a registered loss, by learning to align the SR output to a ground-truth through ShiftNet. We show that by learning deep representations of multiple views, we can super-resolve low-resolution signals and enhance Earth Observation data at scale. Our approach recently topped the European Space Agency's MFSR competition on real-world satellite imagery.

2020-02-15

ArXiv (prépublication)

Modeling Cloud Reflectance Fields using Conditional Generative Adversarial Networks

Mustafa Alghali

We introduce a conditional Generative Adversarial Network (cGAN) approach to generate cloud reflectance fields (CRFs) conditioned on large s… (voir plus)cale meteorological variables such as sea surface temperature and relative humidity. We show that our trained model can generate realistic CRFs from the corresponding meteorological observations, which represents a step towards a data-driven framework for stochastic cloud parameterization.

2020-02-10

ArXiv (prépublication)

Applying Knowledge Transfer for Water Body Segmentation in Peru

Jessenia Gonzalez

Debjani Bhowmick

César Beltrán

2019-12-02

ArXiv (prépublication)

Artificial Intelligence Based Cloud Distributor (AI-CD): Probing Low Cloud Distribution with Generative Adversarial Neural Networks

T. Yuan

H. Song

David Hall

Victor Schmidt

Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks

Victor Schmidt

Alexandra Luccioni

S. Karthik Mukkavilli

Narmada Balasooriya

Jennifer T Chayes

We present a project that aims to generate images that depict accurate, vivid, and personalized outcomes of climate change using Cycle-Consi… (voir plus)stent Adversarial Networks (CycleGANs). By training our CycleGAN model on street-view images of houses before and after extreme weather events (e.g. floods, forest fires, etc.), we learn a mapping that can then be applied to images of locations that have not yet experienced these events. This visual transformation is paired with climate model predictions to assess likelihood and type of climate-related events in the long term (50 years) in order to bring the future closer in the viewers mind. The eventual goal of our project is to enable individuals to make more informed choices about their climate future by creating a more visceral understanding of the effects of climate change, while maintaining scientific credibility by drawing on climate model projections.

2019-05-02

ArXiv (prépublication)

Hierarchical Importance Weighted Autoencoders

Chin-wei Huang

Eeshan Dhekane

Alexandre Lacoste

Aaron Courville

Importance weighted variational inference (Burda et al., 2015) uses multiple i.i.d. samples to have a tighter variational lower bound. We be… (voir plus)lieve a joint proposal has the potential of reducing the number of redundant samples, and introduce a hierarchical structure to induce correlation. The hope is that the proposals would coordinate to make up for the error made by one another to reduce the variance of the importance estimator. Theoretically, we analyze the condition under which convergence of the estimator variance can be connected to convergence of the lower bound. Empirically, we confirm that maximization of the lower bound does implicitly minimize variance. Further analysis shows that this is a result of negative correlation induced by the proposed hierarchical meta sampling scheme, and performance of inference also improves when the number of samples increases.

2019-01-01

ICML (publié)

proceedings.mlr.press