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Cosmic Explorer is a next-generation ground-based gravitational-wave observatory that is being designed in the 2020s and is envisioned to begin operations in the 2030s together with the Einstein Telescope in Europe. The Cosmic Explorer concept currently consists of two widely separated L-shaped observatories in the United States, one with 40 km-long arms and the other with 20 km-long arms. This order of magnitude increase in scale with respect to the LIGO-Virgo-KAGRA observatories will, together with technological improvements, deliver an order of magnitude greater astronomical reach, allowing access to gravitational waves from remnants of the first stars and opening a wide discovery aperture to the novel and unknown. In addition to pushing the reach of gravitational-wave astronomy, Cosmic Explorer endeavors to approach the lifecycle of large scientific facilities in a way that prioritizes mutually beneficial relationships with local and Indigenous communities. This article describes the (scientific, cost and access, and social) criteria that will be used to identify and evaluate locations that could potentially host the Cosmic Explorer observatories. 

Abstract. Urban Green Space (UGS) is vital for improving the public health and sustainability of cities. Vector data on UGS such as open data from governments and OpenStreetMap are available for retrieval by interested users, but the availability of UGS data is still limited on global and temporal scales. This study develops the UGS Extractor, a web-based application for the automatic extraction of UGS given user inputs of Area of Interest and Date of Interest. To accommodate various types of green spaces, such as parks or lawns, the application additionally allows users to set parameters for the minimum size of each UGS and the Minimum Urban Neighbor Density, enabling customization of what qualifies as UGS. The UGS Extractor implements a methodological framework that applies object-based image processing, edge detection and extraction, and image neighborhood analysis on the near real-time 10m Dynamic World collection of Land Use/Land Cover images. The application’s utility was demonstrated through two case studies. In the first, the UGS Extractor accurately mapped major parks when compared to open data sources in New Orleans, USA. In the second, the UGS Extractor demonstrated significant increases in the total area of UGS from 2015 to 2023 in Songdo, South Korea, which consequently improved green space accessibility. These results underscore the UGS Extractor’s utility in extracting specific types of UGS and analyzing their temporal trends. This user-friendly application overall offers higher spatial resolution compared to publicly available satellite-based methods while facilitating temporal studies not possible with vector datasets. 

Abstract Meteorological (MET) data is a crucial input for environmental exposure models. While modeling exposure potential using geospatial technology is a common practice, existing studies infrequently evaluate the impact of input MET data on the level of uncertainty on output results. The objective of this study is to determine the effect of various MET data sources on the potential exposure susceptibility predictions. Three sources of wind data are compared: The North American Regional Reanalysis (NARR) database, meteorological aerodrome reports (METARs) from regional airports, and data from local MET weather stations. These data sources are used as inputs into a machine learning (ML) driven GIS Multi-Criteria Decision Analysis (GIS-MCDA) geospatial model to predict potential exposure to abandoned uranium mine sites in the Navajo Nation. Results indicate significant variations in results derived from different wind data sources. After validating the results from each source using the National Uranium Resource Evaluation (NURE) database in a geographically weighted regression (GWR), METARs data combined with the local MET weather station data showed the highest accuracy, with an average R 2 of 0.74. We conclude that local direct measurement-based data (METARs and MET data) produce a more accurate prediction than the other sources evaluated in the study. This study has the potential to inform future data collection methods, leading to more accurate predictions and better-informed policy decisions surrounding environmental exposure susceptibility and risk assessment.