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.

Currently, video generation devices are simpler to manipulate, more portable and with lower prices. This allowed easy storage and transmission of large amounts of media, such as videos, which has facilitated the analysis of information, independent of human assistance for evaluation and exhaustive search of videos. Virtual reality, robotics, tele-medicine, humanmachine interface and tele-surveillance are applications for these techniques. This paper describes a method for human action recognition in videos using two convolutional neural networks (CNNs). The first one Spatial Stream (trained with frames of the video) and the second one Temporal Stream, trained with stacks of Dense Optical Flow (DOF). Both streams were trained separately and from both of them we generated a classification histogram based on the most frequent class assignment. For final classification, those histograms were combined to produce a single output. The technique was tested in two public action video datasets: Weizmann and UCF Sports. We achieve 84.44% of accuracy on Weizmann dataset for Spatial stream and 78.46% on UCF Sports dataset. For the Weizmann dataset we obtained 91.11% with networks combination.