Automatic mapping of high-risk urban areas for Aedes aegypti infestation based on building facade image analysis.

BACKGROUND: Dengue, Zika, and chikungunya, whose viruses are transmitted mainly by Aedes aegypti, significantly impact human health worldwide. Despite the recent development of promising vaccines against the dengue virus, controlling these arbovirus diseases still depends on mosquito surveillance and control. Nonetheless, several studies have shown that these measures are not sufficiently effective or ineffective. Identifying higher-risk areas in a municipality and directing control efforts towards them could improve it. One tool for this is the premise condition index (PCI); however, its measure requires visiting all buildings. We propose a novel approach capable of predicting the PCI based on facade street-level images, which we call PCINet. METHODOLOGY: Our study was conducted in Campinas, a one million-inhabitant city in São Paulo, Brazil. We surveyed 200 blocks, visited their buildings, and measured the three traditional PCI components (building and backyard conditions and shading), the facade conditions (taking pictures of them), and other characteristics. We trained a deep neural network with the pictures taken, creating a computational model that can predict buildings' conditions based on the view of their facades. We evaluated PCINet in a scenario emulating a real large-scale situation, where the model could be deployed to automatically monitor four regions of Campinas to identify risk areas. PRINCIPAL FINDINGS: PCINet produced reasonable results in differentiating the facade condition into three levels, and it is a scalable strategy to triage large areas. The entire process can be automated through data collection from facade data sources and inferences through PCINet. The facade conditions correlated highly with the building and backyard conditions and reasonably well with shading and backyard conditions. The use of street-level images and PCINet could help to optimize Ae. aegypti surveillance and control, reducing the number of in-person visits necessary to identify buildings, blocks, and neighborhoods at higher risk from mosquito and arbovirus diseases.

Integrating remote sensing and machine learning to detect turbidity anomalies in hydroelectric reservoirs.

Monitoring water quality in reservoirs is essential for the maintenance of aquatic ecosystems and socioeconomic services. In this scenario, the observation of abrupt elevations of physicochemical parameters, such as turbidity and other indicators, can signal anomalies associated with the occurrence of critical events, requiring operational actions and planning to mitigate negative environmental impacts on water resources. This work aims to integrate Machine Learning methods specialized in anomaly detection with data obtained from remote sensing images to identify with high turbidity events in the surface water of the Três Marias Hydroelectric Reservoir. Four distinct threshold-based scenarios were evaluated, in which the overall performance, based on F1-score, showed decreasing trends as the thresholds became more restrictive. In general, the anomaly identification maps generated through the models ratified the applicability of the methods in the diagnosis of surface water in reservoirs in distinct hydrological contexts (dry and wet), effectively identifying locations with anomalous turbidity values.

Behavior Knowledge Space-Based Fusion for Copy-Move Forgery Detection.

The detection of copy-move image tampering is of paramount importance nowadays, mainly due to its potential use for misleading the opinion forming process of the general public. In this paper, we go beyond traditional forgery detectors and aim at combining different properties of copy-move detection approaches by modeling the problem on a multiscale behavior knowledge space, which encodes the output combinations of different techniques as a priori probabilities considering multiple scales of the training data. Afterward, the conditional probabilities missing entries are properly estimated through generative models applied on the existing training data. Finally, we propose different techniques that exploit the multi-directionality of the data to generate the final outcome detection map in a machine learning decision-making fashion. Experimental results on complex data sets, comparing the proposed techniques with a gamut of copy-move detection approaches and other fusion methodologies in the literature, show the effectiveness of the proposed method and its suitability for real-world applications.

Laser printer attribution: exploring new features and beyond.

With a huge amount of printed documents nowadays, identifying their source is useful for criminal investigations and also to authenticate digital copies of a document. In this paper, we propose novel techniques for laser printer attribution. Our solutions do not need very high resolution scanning of the investigated document and explore the multidirectional, multiscale and low-level gradient texture patterns yielded by printing devices. The main contributions of this work are: (1) the description of printed areas using multidirectional and multiscale co-occurring texture patterns; (2) description of texture on low-level gradient areas by a convolution texture gradient filter that emphasizes textures in specific transition areas and (3) the analysis of printer patterns in segments of interest, which we call frames, instead of whole documents or only printed letters. We show by experiments in a well documented dataset that the proposed methods outperform techniques described in the literature and present near-perfect classification accuracy being very promising for deployment in real-world forensic investigations.