Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. Americans are mad as hell about high food prices. They hate paying more at the supermarket even more than they hate paying more at the pump. Food inflation was arguably their main reason for President Donald Trump's win, and Trump's failure to reverse it (while imposing tariffs that accelerate it) is arguably the main reason for his sinking approval ratings. Cost-conscious consumers have been clipping more coupons, dining out less, buying more generic brands, and generally changing their grocery shopping habits to save money.

Timely up-to-date land use/land cover (LULC) maps play a pivotal role in supporting agricultural territory management, environmental monitoring and facilitating well-informed and sustainable decision-making. Typically, when creating a land cover (LC) map, precise ground truth data is collected through time-consuming and expensive field campaigns. This data is then utilized in conjunction with satellite image time series (SITS) through advanced machine learning algorithms to get the final map. Unfortunately, each time this process is repeated (e.g., annually over a region to estimate agricultural production or potential biodiversity loss), new ground truth data must be collected, leading to the complete disregard of previously gathered reference data despite the substantial financial and time investment they have required. How to make value of historical data, from the same or similar study sites, to enhance the current LULC mapping process constitutes a significant challenge that could enable the financial and human-resource efforts invested in previous data campaigns to be valued again. Aiming to tackle this important challenge, we here propose a deep learning framework based on recent advances in domain adaptation and generalization to combine remote sensing and reference data coming from two different domains (e.g. historical data and fresh ones) to ameliorate the current LC mapping process. Our approach, namely REFeD (data Reuse with Effective Feature Disentanglement for land cover mapping), leverages a disentanglement strategy, based on contrastive learning, where invariant and specific per-domain features are derived to recover the intrinsic information related to the downstream LC mapping task and alleviate possible distribution shifts between domains. Additionally, REFeD is equipped with an effective supervision scheme where feature disentanglement is further enforced via multiple levels of supervision at different granularities. The experimental assessment over two study areas covering extremely diverse and contrasted landscapes, namely Koumbia (located in the West-Africa region, in Burkina Faso) and Centre Val de Loire (located in centre Europe, France), underlines the quality of our framework and the obtained findings demonstrate that out-of-year information coming from the same (or similar) study site, at different periods of time, can constitute a valuable additional source of information to enhance the LC mapping process.

ReFED is a national nonprofit working to end food loss and waste across the food system by advancing data-driven solutions to the problem. ReFED leverages data and insights to highlight supply chain inefficiencies and economic opportunities; mobilizes and connects supporters to take targeted action; and catalyzes capital to spur innovation and scale high-impact initiatives. Starting with the 2016 Roadmap to Reduce U.S. Food Waste, ReFED has developed a trusted history of producing first-of-their-kind tools and resources, providing a full-supply-chain picture of U.S. food waste, cost-effective solutions to reduce it, and methods to track progress. In February 2021, ReFED launched its new Roadmap to 2030 and Insights Engine, an online data center designed to serve as the next generation of data, insights, and guidance on U.S. food waste reduction. Solving this problem will have a significant impact on mitigating climate change, optimizing use of water, land, and other resources, and providing meals for the over 50 million people in the United States who currently face food insecurity.

ReFED is a national nonprofit working to end food loss and waste across the food system by advancing data-driven solutions to the problem. ReFED leverages data and insights to highlight supply chain inefficiencies and economic opportunities; mobilizes and connects supporters to take targeted action; and catalyzes capital to spur innovation and scale high-impact initiatives. Starting with the 2016 Roadmap to Reduce U.S. Food Waste, ReFED has developed a trusted history of producing first-of-their-kind tools and resources, providing a full-supply-chain picture of U.S. food waste, cost-effective solutions to reduce it, and methods to track progress. In February 2021, ReFED launched its new Roadmap to 2030 and Insights Engine, an online data center designed to serve as the next generation of data, insights, and guidance on U.S. food waste reduction. Solving this problem will have a significant impact on mitigating climate change, optimizing use of water, land, and other resources, and providing meals for the over 50 million people in the United States who currently face food insecurity.