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This technical note describes the data sources and methodology underpinning a computer system for the automated generation of land use/land cover (LULC) maps of urban areas based on medium-resolution (10–30m/pixel) satellite imagery. The system and maps deploy the LULC taxonomy of the Atlas of Urban Expansion—2016 Edition: open, nonresidential, roads, and four types of residential space. We used supervised machine learning techniques to apply this taxonomy at scale. Distinguishing between recognizable, clearly defined types of land use within a built-up area, rather than merely delineating artificial land cover, enables a huge variety of potential applications for policy, planning, and research. We demonstrate the training and application of machine-learning-based algorithms to characterize LULC over a large spatial and temporal range in a way that avoids many of the onerous constraints and expenses of the traditional LULC mapping process: manual identification and classification of features. This document supersedes the previous technical note, Spatial Characterization of Urban Land Use through Machine Learning, and the methodology described here supersedes our previously reported techniques.

Executive SummaryIn simple terms, every drop of water saved in the U.S. saves energy, and every unit of energy saved saves water. That overlap has come to be known as the "energy-water nexus."For the past 30 years, strategies to conserve energy and increase the efficiency of energy use have been widely pursued. Similar efforts in the conservation and efficient use of water have occurred over the past 20plus years. However, the two communities have historically not worked together in a coherent, collaborative manner, and instead generally created separate but parallel efforts. These separate activities could realize significant benefits from coordination.Recognizing this need for collaborative actions, the American Council for an Energy-Efficient Economy (ACEEE) and the Alliance for Water Efficiency (AWE) secured a grant from the Turner Foundation to bring these two communities together to establish a blueprint for future joint efforts and to envision a policy agenda that could drive actions at the federal, state, local, and watershed levels.This blueprint addresses three broad elements: policy/codes, research, and programs. In developing it, ACEEE and AWE have analyzed and consolidated contributions from approximately fifty individuals, many of whom participated in a full-day workshop in December 2010. The goal of this blueprint and policy agenda is to provide a framework for collaborative action, funding, and policy development.The blueprint further contains eight broad thematic elements, each including a number of action strategies, all of which will require complex collaborations among funding sources, advocates and nonprofit organizations, government agencies at every level, trade associations, energy and water utilities, consumer groups, business, regulatory agencies, universities, national laboratories, policymakers, and the U.S. Congress.By setting bold, innovative new directions that can begin immediately, the developers hope that this blueprint will change the way water and energy are used, measured, and managed, and change the relationships and actions of stake-holders, creating new, more active and visionary coalitions.

This technical note discusses methods for using these data in combination with locally meaningful jurisdictional boundaries to calculate local measurements of indicators on several themes—including access to urban amenities, air quality, biodiversity, flooding, climate change mitigation, heat, and land protection and restoration—relevant to urban decision-makers, researchers, and other stakeholders.