Deforestation Detection with Fully Convolutional Networks in the Amazon Forest from Landsat-8 and Sentinel-2 Images

Torres, Daliana Lobo; Turnes, Javier Noa; Vega, Pedro Juan Soto; Feitosa, Raul Queiroz; Silva, Daniel E.; Marcato, José; Almeida, Cláudio

doi:10.3390/rs13245084

Cited by 36 publications

(35 citation statements)

References 35 publications

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“…Figure 2 is an illustration of our developed DeepLabV3+ network architecture [17][18][19][20][21]. DeepLabV3+ includes an encoder module to extract a compact image representation and a decoder to improve the segmentation.…”

Section: Deeplabv3+ Architecture Descriptionmentioning

confidence: 99%

Deep learning-based segmentation of breast masses using convolutional neural networks

Anghelache Nastase,

Moldovanu,

Moraru

2024

J. Phys.: Conf. Ser.

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Automatic breast tumor segmentation based on convolutional neural networks (CNNs) is significant for the diagnosis and monitoring of breast cancers. CNNs have become an important method for early diagnosis of breast cancer and, thus, can help decrease the mortality rate. In order to assist medical professionals in breast cancer investigation a computerized system based on two encoder-decoder architectures for breast tumor segmentation has been developed. Two pre-trained DeepLabV3+ and U-Net models are proposed. The encoder generates a high-dimensional feature vector while the decoder analyses the low-resolution feature vector provided by the encoder and generates a semantic segmentation mask. Semantic segmentation based on deep learning techniques can overcome the limitations of traditional algorithms. To assess the efficiency of breast ultrasound image segmentation, we compare the segmentation results provided by CNNs against the Local Graph Cut technique (a semi-automatic segmentation method) in the Image Segmenter application. The output segmentation results have been evaluated by using the Dice similarity coefficient that compares the ground truth images provided by the specialists against the predicted segmentation results provided by the CNNs and Local Graph Cut algorithm. The proposed approach is validated on 780 breast ultrasonographic images of the BUSI public database of which 437 are benign and 210 are malignant. The BUSI database provides classification (benign or malignant) labels for ground truth in binary mask images. The average Dice scores computed between the ground truth images against CNNs were as follows: 0.9360 (malignant) and 0.9325 (benign) for the DeepLabV3+ architecture and of 0.6251 (malignant) and 0.6252 (benign) for the U-Net, respectively. When the segmentation results provided by CNNs were compared with the Local Graph Cut segmented images, the Dice scores were 0.9377 (malignant) and 0.9204 (benign) for DeepLabV3+ architecture and 0.6115 (malignant) and 0.6119 (benign) for U-Net, respectively. The results show that the DeepLabV3+ has significantly better segmentation performance and outperforms the U-Net network.

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Section: Deeplabv3+ Architecture Descriptionmentioning

confidence: 99%

Deep learning-based segmentation of breast masses using convolutional neural networks

Anghelache Nastase,

Moldovanu,

Moraru

2024

J. Phys.: Conf. Ser.

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“…Landsat-8, Terra, and Sentinel-2 are the three satellites that are commonly used to develop datasets for forest monitoring systems, as used in Krasovskii et al [12], Wyniawskyj et al [13], and Torres et al [14]. The spatial resolutions of these satellites are listed in Table 1, in which they have been successfully used for various other applications, including forest monitoring systems.…”

Section: Satellite Technology In Forest Monitoring Systemsmentioning

confidence: 99%

Forest Segmentation with Spatial Pyramid Pooling Modules: A Surveillance System Based on Satellite Images

Zulkifley

Abdani

et al. 2023

Forests

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The global deforestation rate continues to worsen each year, and will eventually lead to various negative consequences for humans and the environment. It is essential to develop an effective forest monitoring system to detect any changes in forest areas, in particular, by monitoring the progress of forest conservation efforts. In general, changes in forest status are difficult to annotate manually, whereby the boundaries can be small in size or hard to discern, especially in areas that are bordering residential areas. The previously implemented forest monitoring systems were ineffective due to their use of low-resolution satellite images and the inefficiency of drone-based data that offer a limited field of view. Most government agencies also still rely on manual annotation, which makes the monitoring process time-consuming, tedious, and expensive. Therefore, the goal of this study is to overcome these issues by developing a forest monitoring system that relies on a robust deep semantic segmentation network that is capable of discerning forest boundaries automatically, so that any changes over the years can be tracked. The backbone of this system is based on satellite imaging supplied to a modified U-Net deep architecture to incorporate multi-scale modules to deliver the semantic segmentation output. A dataset of 6048 Landsat-8 satellite sub-images that were taken from eight land parcels of forest areas was collected and annotated, and then further divided into training and testing datasets. The novelty of this system is the optimal integration of the spatial pyramid pooling (SPP) mechanism into the base model, which allows the model to effectively segment forest areas regardless of their varying sizes, patterns, and colors. To investigate the impact of SPP on the forest segmentation system, a set of experiments was conducted by integrating several variants of SPP ranging from two to four parallel paths with different combinations of pooling kernel size, placed at the bottleneck layer of the U-Net model. The results demonstrated the effectiveness of the SPP module in improving the performance of the forest segmentation system by 2.57%, 6.74%, and 7.75% in accuracy (acc), intersection over union (IoU), and F1-score (F1score), respectively. The best SPP variant consists of four parallel paths with a combination of pooling kernel sizes of 2×2, 4×4, 6×6, and 8×8 pixels that produced the highest acc, IoU, and F1score of 86.71%, 75.59%, and 82.88%, respectively. As a result, the multi-scale module improved the proposed forest segmentation system, making it a highly useful system for government and private agencies in tracking any changes in forest areas.

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“…They have led to radical advances in our ability to make predictions in countless fields and have recently been taken up by RS researchers (Zhu et al, 2017) and ecologists working with Earth observation data (Brodrick et al, 2019) to generate novel insights. This includes work on detecting logging trails (Abdi et al, 2022), human settlements (Corbane et al, 2021), deforestation (Torres et al, 2021), forest disturbance (Kislov et al, 2021) and quantifying the properties of terrestrial vegetation (Kattenborn et al, 2021). Importantly, however, these studies have focused on assessing the current state of systems rather than predicting future states.…”

Section: Introductionmentioning

confidence: 99%

Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation

et al. 2022

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1. Tropical forests are subject to diverse deforestation pressures while their conservation is essential to achieve global climate goals. Predicting the location of deforestation is challenging due to the complexity of the natural and human systems involved but accurate and timely forecasts could enable effective planning and on-the-ground enforcement practices to curb deforestation rates. New computer vision technologies based on deep learning can be applied to the increasing volume of Earth observation data to generate novel insights and make predictions with unprecedented accuracy. 2. Here, we demonstrate the ability of deep convolutional neural networks (CNNs) to learn spatiotemporal patterns of deforestation from a limited set of freely available global data layers, including multispectral satellite imagery, the Hansen maps of annual forest change (2001-2020) and the ALOS PALSAR digital surface model, to forecast deforestation (2021). We designed four model architectures, based on 2D CNNs, 3D CNNs, and Convolutional Long Short-Term Memory (ConvLSTM) Recurrent Neural Networks (RNNs), to produce spatial maps that indicate the risk to each forested pixel (~30 m) in the landscape of becoming deforested within the next year. They were trained and tested on data from two ~80,000 km 2 tropical forest regions in the Southern Peruvian Amazon.3. The networks could predict the location of future forest loss to a high degree of accuracy (F 1 = 0.58-0.71). Our best performing model (3D CNN) had the highest pixel-wise accuracy (F 1 = 0.71) when validated on 2020 forest loss (2014-2019 training). Visual interpretation of the mapped forecasts indicated that the network could automatically discern the drivers of forest loss from the input data.For example, pixels around new access routes (e.g. roads) were assigned high risk, whereas this was not the case for recent, concentrated natural loss events (e.g. remote landslides). 4. Convolutional neural networks can harness limited time-series data to predict near-future deforestation patterns, an important step in harnessing the growing volume of satellite remote sensing data to curb global deforestation. The | 2623 Methods in Ecology and Evoluঞon BALL et al.

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Deforestation Detection with Fully Convolutional Networks in the Amazon Forest from Landsat-8 and Sentinel-2 Images

Cited by 36 publications

References 35 publications

Deep learning-based segmentation of breast masses using convolutional neural networks

Deep learning-based segmentation of breast masses using convolutional neural networks

Forest Segmentation with Spatial Pyramid Pooling Modules: A Surveillance System Based on Satellite Images

Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation

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