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Rainforests play an important role in the global ecosystem. However, significant regions of them are facing deforestation and degradation due to several reasons. Diverse government and private initiatives were created to monitor and alert for deforestation increases from remote sensing images, using different ways to deal with the notable amount of generated data. Citizen Science projects can also be used to reach the same goal. Citizen Science consists of scientific research involving nonprofessional volunteers for analyzing, collecting data, and using their computational resources to outcome advancements in science and to increase the public’s understanding of problems in specific knowledge areas such as astronomy, chemistry, mathematics, and physics. In this sense, this work presents a Citizen Science project called ForestEyes, which uses volunteer’s answers through the analysis and classification of remote sensing images to monitor deforestation regions in rainforests. To evaluate the quality of those ...

In this paper, we investigate strategies to accelerate CNNs designed for the JPEG compressed domain. The starting point for our study is a state-of-the-art CNN proposed by Gueguen et al. [1], which is a modified version of the ResNet-50 [2]. However, the changes introduced by Gueguen et al. [1] in the ResNet-50 raised its computational complexity and number of parameters. To alleviate these drawbacks, Santos et al. [3] proposed to feed the network with the lowest frequency DCT coefficients, thus losing image details irretrievably. Here, we explore smart strategies to reduce the network computation complexity without sacrificing rich information provided by the DCT coefficients. We conducted experiments on the ImageNet dataset, both in a subset and in the whole. Our results on both datasets showed that learning how to combine all DCT inputs in a data-driven fashion is better than discarding them by hand. Also, we found that skipping some stages of the network is beneficial, proving to be effective for re...

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