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Urban green spaces (UGSs) provide essential environmental services for the well-being of ecosystems and society. Due to the constant environmental, social, and economic transformations of cities, UGSs pose new challenges for management, particularly in fast-growing metropolitan areas. With technological advancement and the evolution of deep learning, it is possible to optimize the acquisition of UGS inventories through the detection of geometric patterns present in satellite imagery. This research evaluates two deep learning model techniques for semantic segmentation of UGS polygons with the use of different convolutional neural network encoders on the U-Net architecture and very high resolution (VHR) imagery to obtain updated information on UGS polygons at the metropolitan area level. The best model yielded a Dice coefficient of 0.57, IoU of 0.75, recall of 0.80, and kappa coefficient of 0.94 with an overall accuracy of 0.97, which reflects a reliable performance of the network in detecting patterns that make up the varied geometry of UGSs. A complete database of UGS polygons was quantified and categorized by types with location and delimited by municipality, allowing for the standardization of the information at the metropolitan level, which will be useful for comparative analysis with a homogenized and updated database. This is of particular interest to urban planners and UGS decision-makers.

Biomass is a renewable resource that has attractive characteristics for manufacturing many types of products such as biofuels, specialty chemicals, and energy generation. Several studies have addressed diverse important features such as new technologies for biomass treatment, approaches for potential location of refineries, models to evaluate existing relationships between availability and price, among others. However, two important issues need to be considered: potential locations for biomass processing systems facilities considering geographic constraints, as well as uncertainty associated with biomass availability over time. Thus, this paper presents an approach to determine viable facility locations for supply chains based on residual biomass. This work has used a tool based on geographic information systems in order to involve environmental, social and geographic restrictions. Some of considered environmental features are exclusion of protected areas, vegetation type according with human activities, and water bodies location. Also, the approach considers technical constraints given by the terrain slope, wetland zones, distance to transportation infrastructure, distance to power grids and power plants, etc. In addition, social constrains such as distance to urban and rural communities, and historic monuments are included as well. Additionally, climatic aspects such as frequency of drought and hurricanes have been also taken into account. Besides, this work has considered the effect of biomass uncertainty through several scenarios, based on historical data for residual biomass availability, to include variations over time of this factor on biomass processing systems. The proposed geographic information system was tested for a nationwide case study in Mexico to determine viable regions to locate biomass processing systems facilities. The results show that biomass availability variations over time due to uncertainty have a strong effect on viable regions to install facilities in a supply chain based on biomass. Furthermore, the results also illustrate the influence of the type of raw material on the number of potential locations. This method is useful to define potential locations to be taken into account in problems like supply chain design or planning problem because it is possible to decrease considerably the number of options to choose accounting social, environmental, and geographic issues as well as uncertainty associated with biomass availability.

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Aerosol and meteorological remote sensing data could be used to assess the distribution of urban and regional fine particulate matter (PM2.5), especially in locations where there are few or no ground-based observations, such as Latin America. The objective of this study is to evaluate the ability of Modern-Era Retrospective Analysis for Research and Application, version 2 (MERRA-2) aerosol components to represent PM2.5 ground concentrations and to develop and validate an ensemble neural network (ENN) model that uses MERRA-2 aerosol and meteorology products to estimate the monthly average of PM2.5 ground concentrations in the Monterrey Metropolitan Area (MMA), which is the main urban area in Northeastern Mexico (NEM). The project involves the application of the ENN model to a regional domain that includes not only the MMA but also other municipalities in NEM in the period from January 2010 to December 2014. Aerosol optical depth (AOD), temperature, relative humidity, dust PM2.5, sea salt PM2.5, black carbon (BC), organic carbon (OC), and sulfate (SO42−) reanalysis data were identified as factors that significantly influenced PM2.5 concentrations. The ENN estimated a PM2.5 monthly mean of 25.62 μg m−3 during the entire period. The results of the comparison between the ENN and ground measurements were as follows: correlation coefficient R ~ 0.90; root mean square error = 1.81 μg m−3; mean absolute error = 1.31 μg m−3. Overall, the PM2.5 levels were higher in winter and spring. The highest PM2.5 levels were located in the MMA, which is the major source of air pollution throughout this area. The estimated data indicated that PM2.5 was not distributed uniformly throughout the region but varied both spatially and temporally. These results led to the conclusion that the magnitude of air pollution varies among seasons and regions, and it is correlated with meteorological factors. The methodology developed in this study could be used to identify new monitoring sites and address information gaps.