We address the task of mapping deforested areas in the Brazilian Amazon. Accurate maps are an important tool for informing effective deforestation containment policies. The main existing approaches to this task are largely manual, requiring significant effort by trained experts. To reduce this effort, we propose a fully automatic approach based on spatio-temporal deep convolutional neural networks. We introduce several domain-specific components, including approaches for: image preprocessing; handling image noise, such as clouds and shadow; and constructing the training data set. We show that our preprocessing protocol reduces the impact of noise in the training data set. Furthermore, we propose two spatio-temporal variations of the U-Net architecture, which make it possible to incorporate both spatial and temporal contexts. Using a large, real-world data set, we show that our method outperforms a traditional U-Net architecture, thus achieving approximately 95% accuracy.

Mining has played an important role in the economies of South American countries. Although industrial mining prevails in most countries, the expansion of garimpo activity has increased substantially. Recently, Brazil exhibited two moments of garimpo dominance over industrial mining: 1989–1997 and 2019–2022. While industrial mining sites occupied ~ 360 km² in 1985 but increased to 1800 km² in 2022, a 5-fold increase, garimpo mining area increased by ~ 1200%, from ~ 218 km² in 1985 to ~ 2627 km² in 2022. More than 91% of this activity is concentrated in the Amazon. Where almost 40% of the sites are five years old or younger, this proportion increases to 62% within Indigenous lands (ILs). Regarding the legal aspect, at least 77% of the 2022 extraction sites showed explicit signs of illegality. Particular attention must be given to the Kayapo, Munduruku, and Yanomami ILs. Together, they concentrate over 90% of the garimpo across ILs.

Cerrado is the second largest biome in Brazil, covering about 2 million km${}^2$. This biome has experienced land use and land cover changes at high rates due to agricultural expansion so that more than $50\%$ of its natural vegetation has already been removed. Therefore, it is crucial to provide technology capable of controlling and monitoring the Cerrado vegetation suppression in order to undertake the environmental conservation policies. Within this context, this work aims to develop a new methodology to detect deforestation in Cerrado through the combination of two Deep Learning (DL) architectures, Long Short-Term Memory (LSTM) and U-Net, and using Landsat and Sentinel image time series. In our proposed method, the LSTM evaluates the time series in relation to the time axis to create a deforestation probability map, which is spatially analyzed by the U-Net algorithm alongside the terrain slope to produce final deforestation maps. The method was applied in two different study areas, which better represent the main deforestation patterns present in Cerrado. The resultant deforestation maps based on cost-free Sentinel-2 images achieved high accuracy metrics, peaking at an overall accuracy of $99.81\%\pm 0.21$ and F1-Score of $0.8795\pm 0.1180$. In addition, the proposed method showed strong potential to automate the PRODES project, which provides the official Cerrado yearly deforestation maps based on visual interpretation.

Deep learning-based models for building delineation from remotely sensed images face the challenge of producing precise and regular building outlines. This study investigates the combination of normalized digital surface models (nDSMs) with aerial images to optimize the extraction of building polygons using the frame field learning method. Results are evaluated at pixel, object, and polygon levels. In addition, an analysis is performed to assess the statistical deviations in the number of vertices of building polygons compared with the reference. The comparison of the number of vertices focuses on finding the output polygons that are the easiest to edit by human analysts in operational applications. It can serve as guidance to reduce the post-processing workload for obtaining high-accuracy building footprints. Experiments conducted in Enschede, the Netherlands, demonstrate that by introducing nDSM, the method could reduce the number of false positives and prevent missing the real buildings on the ground. The positional accuracy and shape similarity was improved, resulting in better-aligned building polygons. The method achieved a mean intersection over union (IoU) of 0.80 with the fused data (RGB + nDSM) against an IoU of 0.57 with the baseline (using RGB only) in the same area. A qualitative analysis of the results shows that the investigated model predicts more precise and regular polygons for large and complex structures.

Dengue fever represents a great challenge for many countries, and methodologies to prevent and/or control its transmission have been largely discussed by the research community. Modeling is a powerful tool to understand epidemic dynamics and to evaluate costs, benefits and effectiveness of control strategies. In order to assist decision-makers and researchers in the evaluation of different methodologies, we developed DengueME, a collaborative open source platform to simulate dengue disease and its vector's dynamics. DengueME provides a series of compartmental and individual-based models, implemented over a GIS database, that represents the Aedes aegypti's life cycle, human demography, human mobility, urban landscape and dengue transmission. The platform is designed to allow easy simulation of intervention scenarios. A GUI was developed to facilitate model configuration and data input.