Detecting individual trees and quantifying their biomass is crucial for carbon accounting procedures at the stand, landscape, and national levels. A significant challenge for many organizations is the amount of effort necessary to document carbon storage levels, especially in terms of human labor. To advance towards the goal of efficiently assessing the carbon content of forest, we evaluate methods to detect trees from high-resolution images taken from unoccupied aerial systems (UAS). In the process, we introduce the Digital Elevated Vegetation Model (DEVM), a representation that combines multispectral images, digital surface models, and digital terrain models. We show that the DEVM facilitates the development of refined synthetic data to detect individual trees using deep learning-based approaches. We carried out experiments in two tree fields located in different countries. Simultaneously, we perform comparisons among an array of classical and deep learning-based methods highlighting the precision and reliability of the DEVM.

One of the challenges in the fight against poverty is the precise localization and assessment of vulnerable communities’ sprawl. The characterization of vulnerability is traditionally accomplished using nationwide census exercises, a burdensome process that requires field visits by trained personnel. Unfortunately, most countrywide censuses exercises are conducted only sporadically, making it difficult to track the short-term effect of policies to reduce poverty. This paper introduces a definition of vulnerability following UN-Habitat criteria, assesses different CNN machine learning architectures, and establishes a mapping between satellite images and survey data. Starting with the information corresponding to the 2,178,508 residential blocks recorded in the 2010 Mexican census and multispectral Landsat-7 images, multiple CNN architectures are explored. The best performance is obtained with EfficientNet-B3 achieving an area under the ROC and Precision-Recall curves of 0.9421 and 0.9457, respectively. This article shows that publicly available information, in the form of census data and satellite images, along with standard CNN architectures, may be employed as a stepping stone for the countrywide characterization of vulnerability at the residential block level.

Knowing the most likely clinical prognosis for a patient infected with SARS-Cov-2 could offer guidelines for tracking their medical evolution, improving attention, and assigning resources. Aiming to assess a patient’s status quantitatively, we explore the analysis of existing clinical information using data-driven methods. Our goal is to extract the characteristics distinguishing between those COVID-19 patients that improve and those who die. In our approach, we select the relevant features using the algorithm of Boruta, a wrapper framework that takes input from classifiers generating relevance assessment of the predictors. Using the extracted features, we train machine learning classifiers, including Random Forests, Support Vector Machine, Extreme Gradient Boosting, and Neural Networks. We assess the performance of the classifiers using Precision-Recall and ROC analysis, establishing the ranges at which risk assessment permits effective decision-making. Our research highlights that local regions present unique sets of essential features, that it is possible to construct effective classifiers based on clinical data, and that an ensemble of classifiers results in the best performing discriminant.