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.

The environmental health of Latin America and the Caribbean (LAC) is crucial to the survival of the planet. LAC countries occupy 13% of the Earths landmass yet contain 60% of the terrestrial life. The region is particularly brittle as climate change looms on the horizon, and its economic reliance on exploiting natural resources is accelerating its biodiversity loss. Central to these problems is that LAC is the most economically unequal region globally. Thus, it faces unique challenges in promoting sustainable development. This paper explores whether and how Artificial Intelligence (AI) may provide methods to accelerate the changes needed to increase resilience and facilitate adaptation. Starting with a systematic mapping of the research on AI for sustainability in LAC, we present a diagnosis of the current situation structured along the proposed axes of climate change, human vulnerability, and biodiversity. Then, we give some illustrative examples of potential directions for further work with applicability to the region. Due to its often overlooked resources, capabilities, and particularly fragile geolocation, LAC is called to play an oversize role in the planets sustainability in the coming decades.