IceNet is a new probabilistic, deep learning sea ice forecasting system “trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged sea ice concentration maps.” It’s a U-Net model that uses 50 climate variables as input, and outputs discrete probability distributions for three different sea ice concentration classes at each grid cell. Coolest (haha) part: “IceNet runs over 2000 times faster on a laptop than SEAS5 running on a supercomputer, taking less than ten seconds on a single graphics processing unit.” Practical use cases are in planning shipping routes and in avoiding conflicts between ships and migrating walruses and whales. Pretty cool.

Climate Change AI is launching an Innovation Grants program that “will fund year-long research projects at the intersection of climate change and machine learning for up to USD 150K per project, for a total of USD 1.8M.” Their areas of interest include AI approaches to: mitigation; adaptation; climate science; low-carbon technology research and development; behavioral and social science related to climate; and AI governance in the context of climate change. The submission deadline is October 15th.

Earlier this year, I wrote about the climate opportunity of gargantuan AI models: like many other types of workloads, the offline training of AI models could be scheduled dynamically based on electricity market signals of over- or under-supply. This way, these power-hungry datacenters can provide demand-side response for balancing the power grid — an increasingly important problem with the growth of renewables — so that we’ll be less dependent on supply-side balancing from coal and gas plants. I’m excited to share that over the past few months, I co-supervised Hongyu He’s BSc thesis project at the VU Amsterdam’s @Large Research group on exactly this topic. In his 158-page (!) thesis, Hongyu extended OpenDC, a datacenter simulator I helped develop during my own BSc, to incorporate electricity price signals from Dexter’s asset optimization product into the virtual datacenter’s workload scheduler. He then simulated different ways for datacenters to participate directly in power markets, and found that these could be profitable (and therefore helpful in balancing the grid). Hongyu’s full thesis is on arXiv: How Can Datacenters Join the Smart Grid to Address the Climate Crisis? This simulation work is an important step for convincing stakeholders to pilot and deploy this in the real world; I hope to have more to share on that in the future.

After launching their wiki last month, Climate Change AI has now set up a workshop papers repository featuring the work presented at all eight previous Tackling Climate Change with AI events, held across conferences like ICML, ICLR and NeurIPS. All papers are tagged with searchable subject areas and whether the paper won an award. The power and energy tag, for example, will be a great source for our literature reviews at Dexter!

There is now a Climate Change AI Wiki. It has sections on climate + machine learning research into mitigation, adaptation, climate science, and tools for action. Some of my favorite pages so far are the ones on electricity systems, transportation, forestry and other land use, and weather forecasting. A good website to bookmark!

Stephen Rasp of agriculture climate change adaptation platform ClimateAi has launched Pangeo ML Datasets, a website that collects weather and climate datasets for AI research. It includes both raw datasets and datasets already preprocessed specifically for training machine learning models.

Towards Tracking the Emissions of Every Power Plant on the Planet by Couture et al. (2020) won the Best Pathway to Impact award at the NeurIPS 2020 CCAI workshop. Supported by Al Gore’s Climate TRACE (and grants from Google.org and Bloomberg Philanthropies), the project “[uses] machine learning to infer power generation and emissions from visible and thermal power plant signatures in satellite images.” In this initial paper, the authors present models that can predict whether a power plant is currently on or off from a single satellite image; their best model, a convolutional neural network, gets a mean average precision of 81% on this binary classification task. Interestingly, they find that the “vapor plume” (steam) from a power plant’s cooling system is a better indicator for its emissions than the “smoke plume” (greenhouse gasses) coming out of its main chimney.

The United Kingdom Centre for AI & Climate organized a workshop on AI for Net Zero electricity in November 2020. Outcomes of the workshop include five high-level recommendations: (1) develop a vision and roadmap for the role of AI in reaching a net-zero electricity system; (2) create and distribute energy-AI training resources and build links with universities; (3) invest in explainable AI; (4) coordinate the development of data standards and access to key datasets; and (5) for government agencies to engage more closely with businesses. These all sound very reasonable to me — much better than most high-level “how will we sprinkle AI into this?” business strategies I’ve read! It’d be interesting to see if the European Union, the United States, China, India, etc. also have strategies on this.

Climate Change AI’s NeurIPS 2020 Workshop on Tackling Climate Change with Machine Learning happened last week, featuring nearly 100 papers and proposals. I haven’t had the time to properly read through everything yet, but it looks like there’s a lot of great work to dive into over the holidays!

Open Climate Fix, which works on solar PV mapping and nowcasting, among other things, is hiring an ML Research Engineer to “research cutting-edge ML to help mitigate climate change” and build out open-source services. Go apply! (#nospon)

Researchers from Andrew Ng’s Stanford ML Group have “developed and deployed a deep learning model called OGNet to detect oil and gas infrastructure in aerial imagery.” They’ve used it to create an open dataset with 7,000+ images of nearly 150 oil refineries — including several facilities that were not yet included in existing public datasets — which they hope will make it easier to attribute satellite-detected methane emissions to their sources on the ground. The paper by Sheng & Irving et al. (2020) will be published at the 2020 NeurIPS workshop on Tackling Climate Change with Machine Learning. Research like this has the potential to become a key tool for climate policy makers and enforcers.

In collaboration with the NGO Global Witness, which investigates human rights abuses surrounding conflicts over natural resources, Laradji et al. (2020) built a machine learning model to track illegal cattle ranching based on high-resolution (40cm) satellite imagery. “Cattle farming is responsible for 8.8% of greenhouse gas emissions worldwide. … While some regulations are in place for preserving the Amazon against deforestation, these are being flouted in various ways, hence the need to scale and automate the monitoring of cattle ranching activities.” The paper has been accepted to the Tackling Climate Change with ML Workshop at NeurIPS 2020, and training code is available on GitHub at IssamLaradji/cownter_strike.

For Google’s The Keyword blog, Alicia Cormie wrote about how Los Angeles’ first-ever City Forest Officer Rachel Malarich is using the company’s Tree Canopy Lab to decide where to plant trees. Tree Canopy Lab uses satellite imagery to automatically map a city’s tree cover density in different areas, which Cormie uses to find the communities most in need of more trees. Trees in cities are awesome: they improve air quality and reduce the effects of extreme climate change-induced heat. Plant more trees in cities! It’s great to see an ML-powered project being used to help with this.

Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review (Antonopoulos et al., 2020) is a new 35-page paper examining how AI can be used to balance an electrical grid that is increasingly powered by variable renewable sources like wind and solar. “This work provides an overview of AI methods utilised for [Demand Response] applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects.”

Facebook AI Research and CMU’s Department of Chemical Engineering have teamed up to launch Open Catalyst Project, a collaborative battery technology research effort in “using AI to model and discover new catalysts to address the energy challenges posed by climate change.” Practically, this comes in the form of a dataset, open-source baseline models, and a leader board.

Montañez et al. (2020) have developed a method and GUI-enabled application for automatically segmenting defects in solar PV modules from thermal images captured using drones. As over 100 gigawatts of new solar power installations are being deployed across the world every year, AI-enabled products like this will become more and more useful.

Some recent progress on nowcasting (predicting over the next few hours) the locations of clouds: Berthomier et al. (2020) compared the effectiveness of several deep learning models for the task, and Jack Kelly of Open Climate Fix open-sourced a Python notebook that approaches the same problem using optimal flow. This work is a step towards improving predictions of solar panels’ power output, an important task for operators as an increasing fraction of the electricity supply on their grids transitions to solar.

Rana and Vashney (2020) developed ePSA (e-Plantation Site Assistant), a recommender system that “combines physics-based/traditional forestry science knowledge with machine learning” to find the optimal spots to plant trees when reforesting an area. The ML portion is an XGBoost decision tree ensemble to calculate deforestation probabilities based on historical data. After testing the system around northern India, they found that “in 26 out of 30 locations, forest officials found the recommendations of the app very useful for selecting the right site for planting trees.”

AmazonNET by Mohla et al. (2020) is new a model that detects and segments rainforest burn marks due to wildfires from satellite imagery. Slash-and-burn is a popular technique for clearing away large swaths of the Amazon rainforest for farming use, which additionally is a common cause of wildfires in the region. Detecting and controlling these fires from the ground is difficult, so this is very important work: automated segmentation of these burn scars will allow for more complete and objective tracking, and for more effective prevention. The most interesting contribution here is that the proposed model learns from a dataset with very noisy labels.

Yossi Matias wrote about Google’s progress on flood forecasting in India and Bangladesh for the company’s The Keyword blog. They’re now covering an area of 250,000 square kilometers with a population of 200 million people — a 20x increase from last year — to whom Google has sent 30 million river flood warning notifications in total so far. The technical write-up by Sella Nevo contains details of the AI that went into this, such as a model to infer elevation levels from satellite imagery and a multi-stage model to estimate river profiles. Where AmazonNET helps with climate change prevention, this work sits solidly on the adaptation side.

And finally, on the climate measurement side: Rahnemoonfar et al. (2020) introduced a new convolutional network that can detect — and quite successfully estimate the thickness of — different ice layers inside glaciers and ice sheets. Deep learning for science!

️New dataset from Nielsen et al. (2020): CloudCast: A Satellite-Based Dataset and Baseline for Forecasting Clouds is “the first publicly available dataset with high-resolution cloud types on a high temporal granularity to the authors’ best knowledge.” The dataset has over 70k images, each annotated with 10 different cloud types, that were recoded in 15-minute increments from January 1st, 2017 to December 31st, 2018. Sounds like it could be very useful for solar PV nowcasting models (DT #18, #40).

New paper from Henderson et al. (2020): Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning. The authors propose a framework and open-source software tool for measuring the energy use of ML models trained on Linux systems with Intel chips and NVIDIA GPUs. They also use electricityMap to estimate carbon emissions based on energy use, but because datacenters increasingly generate their own solar or wind power, and their electricity’s carbon intensity may therefore not be the same as that of the grid in their region, I’d take those numbers with a grain of salt. Anyway, I first came across the tool about half a year ago, and I’m glad it has picked up some steam since then. Consider giving it a star on GitHub and using it for your next paper or integrating into your company’s tooling!

SunDown is a “a sensorless approach designed to detect per-panel faults in residential solar arrays” by Feng et al. (2020). Trained on years of solar generation data from homes, it “leverages correlations between the power produced by adjacent panels to detect deviations from expected behavior,” detecting faults and electrical failures with > 99% accuracy. This also sounds like an app waiting to happen!

Brandt and Stolle (2020) propose a new method for detecting scattered trees outside of dense forests, “important for carbon sequestration, supporting livelihoods, maintaining ecosystem integrity, and climate change adaptation and mitigation.”

Williams et al. (2020) present a pipeline for tracking “early-successional” species of trees that are the first to appear after an area has been logged, when the forest begins to recover. They process videos from unmanned aerial vehicles (UAVs) and use SVMs and random forests to identify the crowns of different species of trees in Indonesia.

Omdena, another platform “where AI engineers and domain experts collaborate to build solutions to real-world problems,” hosted a competition together with the UN Refugee Agency (UNHCR) to predict forced displacement, violent conflicts, and climate change in Somalia. The resulting models can be used to help optimize the allocation of resources. The competition has finished and the result are available here.

After tech companies came under fire a few months ago for their work with oil and gas companies, leading to the #techwontdrill it pledge (see DT #33), Google has now committed that it will no longer enter into new agreements to “build custom AI/ML algorithms to facilitate upstream extraction in the oil and gas industry.” This information comes from a new Greenpeace report which I’ll cover in more detail in a future edition of DT.

Flo Wirtz of Open Climate Fix wrote an update on their solar PV nowcasting (how much electricity will be produced in the next few hours?) and mapping (where are all solar panels located?) projects. Both projects hit quite a few proof points, including data acquisition and external validation; they also have a new co-funding award from the European Space Agency.

Climate Change 101 is CCAI’s 50-slide deck on the basics of climate science, aimed at ML/AI researchers.

The US Department of Energy has announced that it’s investing $30 million in research on ML/AI research on energy systems. Two specific areas of interest are ML for predictive modeling and simulation (presumably stuff like DeepMind’s wind farm power output predictions, see DT #8) and AI for “decision support” in managing complex systems in general.

Cool climate-adjacent paper by Biermann et al. (2020): Finding Plastic Patches in Coastal Waters using Optical Satellite Data

Job alert: Ryan Orbuch’s team at Stripe is hiring two fullstack engineers and a product designer, “to make it easy for users to have a real impact on climate.” Stripe’s execution is always top-tier so if you’re a designer or software engineer looking for a change of employment, this is the most sure-fire way you can help the climate. (Not sponsored.)

In a new paper for Nature, Zhang et al. (2020) used Gaussian processes to forecast the health and remaining useful life (RUL) of Lithium-ion batteries, the type of battery used in smartphones and electric cars, based on real-time, non-invasive electrochemical impedance spectroscopy (EIS) measurements. Reliable EIS-based RUL estimation can be used to improve the usability and recyclability of these batteries, which will be critical as we electrify the economy.