Deep Learning for Climate Adaptation: Detecting Drought from Space
TL;DR — our new project helps drought resilience through deep learning on satellite images—join our efforts here.
The Drought Watch benchmark
The Weights & Biases Benchmarks feature offers a centralized platform for open and efficient collaboration on deep learning projects. The latest is Drought Watch, developed with Andrew Hobbs, Nathan Jensen, and Jin Baek through our recent Applied Deep Learning class and the Computer Vision for Global Challenges workshop at CVPR 2019, with data and support from many organizations [1]. The goal of this project is to improve drought detection from satellite images so index insurance can more effectively support Northern Kenyan families, especially as climate conditions worsen.
The challenge
The dataset contains about 100,000 satellite images of Northern Kenya in 10 frequency bands, collected by the International Livestock Research Institute. Local experts (pastoralists, or nomadic herders) manually labeled the forage quality at the corresponding geolocations—specifically, the number of cows from {0, 1, 2, 3+} that the location at the center of the satellite image can feed. Each satellite image is 1.95km across, and each pixel in it represents a 30 meter square. Pastoralists estimate the forage quality within 20 meters when they stand on location, an area slightly larger than a pixel in the full 65x65-pixel satellite image. The satellite images thus provide a lot of extra context, which may prove useful since forage quality is correlated across space. The challenge is to learn a mapping from a satellite image to forage quality so we can more accurately predict drought conditions. Furthermore, the current labeling is very sparse, and we want dense predictions of forage quality at any pixel in a satellite image, not just at the center.
The index insurance strategy
Index insurance programs [2] help thousands of pastoralists—farmers in Northern Kenya who travel with their cattle searching for forage. The programs monitor environmental conditions and use their predictions of drought to send resources to families in affected areas (e.g. money so herders can keep their animals alive, replace those that die, and/or invest in alternatives). A signature example is the Kenya Livestock Insurance Programme (KLIP), launched in 2014 to protect pastoralists entirely reliant on livestock, which has covered over 150,000 herders [3]. Such programs currently use a normalized differenced vegetation index (NDVI) from near-infrared and red bands of satellite imagery. This does not distinguish between edible and inedible plants and only considers two of the ten available frequency bands. We hope to collaborate on a computer vision model that can better leverage satellite imagery and this large expertly-labeled dataset. With more accurate models, insurance companies can monitor drought conditions—and send resources to families in the right areas—more effectively, lowering costs and making the program accessible to more farmers.
Long-term impact opportunity
According to the UN, droughts and floods caused by climate change are already devastating agriculture in developing countries. This exacerbates hunger and poverty, as 75% of the poor in these countries rely on agriculture to survive. Increasingly extreme weather patterns threaten over 50 million pastoralists in Africa, and countries including Ethiopia, Niger, and West Africa seek to replicate KLIP, with a target of reaching 10 million herders. Another initiative, the G7 InsuResilience, intends to reach 400 million households worldwide by 2020 [4]. Index insurance and resilience-based social protection programs are a promising sustainable strategy for managing and reducing poverty in the long-term. Improving the accuracy of models like this one is an opportunity to make a concrete, scalable impact on many people’s lives and our collective climate resilience—all while honing your deep learning skills.
Code for the changing climate
You can contribute to this project by joining the benchmark here.
You will see simple instructions to clone the repo, download the data, and start training models in a few lines of code. As you run training scripts, you’ll see a link to a wandb page for each run, showing real-time graphs of relevant metrics. In the browser, your results will show up in the "Project workspace" tab to the left. With wandb, you can easily visualize the training and explore various hyperparameters and architectures. We encourage you to include your code and share notes, findings, or observations about your process. You can use this platform to see what others have tried, discuss ideas, and synthesize different approaches. We hope to grow a collaborative community to learn, advance the state of the art, and build a better future together.
— Citations & Notes —
[1] The data used in this research was collected through a research collaboration between the International Livestock Research Institute, Cornell University, and UC San Diego. It was supported by the Atkinson Centre for a Sustainable Future’s Academic Venture Fund, Australian Aid through the AusAID Development Research Awards Scheme Agreement No. 66138, the National Science Foundation (0832782, 1059284, 1522054), and ARO grant W911-NF-14-1-0498.
[2] How does index insurance like this work? Agricultural index insurance uses a common metric like rainfall, average yield per area, or vegetation growth to statistically model crop or livestock production with varying accuracy and computational cost. Indices that can be measured remotely, like rainfall or drought conditions from satellite, have lower computational costs—we can increase prediction accuracy through various post-processing techniques without needing to make additional local measurements. By relying on a shared, external, and maximally objective metric, index insurance avoids two main issues with regular insurance (aside from high cost): adverse selection (where only the folks more likely to suffer losses pay for the insurance) and moral hazard (counterproductive action with the intent of collecting a payout). This leaves the main cost: the basis risk, or the error between the index’s estimate and an individual farmer’s real losses. As agricultural indices of conditions like drought become more accurate with technological advances (in remote sensing, satellite imaging, and now computer vision and deep learning), the basis risk declines, reducing the cost of the program and making it accessible to more farmers. To measure the effectiveness of index insurance programs, economists consider the Minimum Quality Standard, comparing the program’s value over time—in terms of potential for income stabilization—to no insurance program and equivalent direct cash transfer. They also study “ex-ante” effects, increased investments in future productivity that pastoralists are able to make with the knowledge of coverage. You can learn more here.
[4] https://basis.ucdavis.edu/agricultural-index-insurance-economic-development