ViGEO: an Assessment of Vision GNNs in Earth Observation

Satellite missions and Earth Observation (EO) systems represent fundamental assets for environmental monitoring and the timely identification of catastrophic events, long-term monitoring of both natural resources and human-made assets, such as vegetation, water bodies, forests as well as buildings. Different EO missions enables the collection of information on several spectral bandwidths, such as MODIS, Sentinel-1 and Sentinel-2. Thus, given the recent advances of machine learning, computer vision and the availability of labeled data, researchers demonstrated the feasibility and the precision of land-use monitoring systems and remote sensing image classification through the use of deep neural networks. Such systems may help domain experts and governments in constant environmental monitoring, enabling timely intervention in case of catastrophic events (e.g., forest wildfire in a remote area). Despite the recent advances in the field of computer vision, many works limit their analysis on Convolutional Neural Networks (CNNs) and, more recently, to vision transformers (ViTs). Given the recent successes of Graph Neural Networks (GNNs) on non-graph data, such as time-series and images, we investigate the performances of a recent Vision GNN architecture (ViG) applied to the task of land cover classification. The experimental results show that ViG achieves state-of-the-art performances in multiclass and multilabel classification contexts, surpassing both ViT and ResNet on large-scale benchmarks.Comment: Accepted at SSTDM 2023 workshop, held in conjunction with ICDM 2023 conferenc

A Data-Driven Based Dynamic Rebalancing Methodology for Bike Sharing Systems

Mobility in cities is a fundamental asset and opens several problems in decision making and the creation of new services for citizens. In the last years, transportation sharing systems have been continuously growing. Among these, bike sharing systems became commonly adopted. There exist two different categories of bike sharing systems: station-based systems and free-floating services. In this paper, we concentrate our analyses on station-based systems. Such systems require periodic rebalancing operations to guarantee good quality of service and system usability by moving bicycles from full stations to empty stations. In particular, in this paper, we propose a dynamic bicycle rebalancing methodology based on frequent pattern mining and its implementation. The extracted patterns represent frequent unbalanced situations among nearby stations. They are used to predict upcoming critical statuses and plan the most effective rebalancing operations using an entirely data-driven approach. Experiments performed on real data of the Barcelona bike sharing system show the effectiveness of the proposed approach

Double-Step U-Net: A Deep Learning-Based Approach for the Estimation of Wildfire Damage Severity through Sentinel-2 Satellite Data

Wildfire damage severity census is a crucial activity for estimating monetary losses and for planning a prompt restoration of the affected areas. It consists in assigning, after a wildfire, a numerical damage/severity level, between 0 and 4, to each sub-area of the hit area. While burned area identification has been automatized by means of machine learning algorithms, the wildfire damage severity census operation is usually still performed manually and requires a significant effort of domain experts through the analysis of imagery and, sometimes, on-site missions. In this paper, we propose a novel supervised learning approach for the automatic estimation of the damage/severity level of the hit areas after the wildfire extinction. Specifically, the proposed approach, leveraging on the combination of a classification algorithm and a regression one, predicts the damage/severity level of the sub-areas of the area under analysis by processing a single post-fire satellite acquisition. Our approach has been validated in five different European countries and on 21 wildfires. It has proved to be robust for the application in several geographical contexts presenting similar geological aspects

Density-based Clustering by Means of Bridge Point Identification

Density-based clustering focuses on defining clusters consisting of contiguous regions characterized by similar densities of points. Traditional approaches identify core points first, whereas more recent ones initially identify the cluster borders and then propagate cluster labels within the delimited regions. Both strategies encounter issues in presence of multi-density regions or when clusters are characterized by noisy borders. To overcome the above issues, we present a new clustering algorithm that relies on the concept of bridge point. A bridge point is a point whose neighborhood includes points of different clusters. The key idea is to use bridge points, rather than border points, to partition points into clusters. We have proved that a correct bridge point identification yields a cluster separation consistent with the expectation. To correctly identify bridge points in absence of a priori cluster information we leverage an established unsupervised outlier detection algorithm. Specifically, we empirically show that, in most cases, the detected outliers are actually a superset of the bridge point set. Therefore, to define clusters we spread cluster labels like a wildfire until an outlier, acting as a candidate bridge point, is reached. The proposed algorithm performs statistically better than state-of-the-art methods on a large set of benchmark datasets and is particularly robust to the presence of intra-cluster multiple densities and noisy borders

CaBuAr: California burned areas dataset for delineation [Software and Data Sets]

Forest wildfires represent one of the catastrophic events that, over the last decades, have caused huge environmental and humanitarian damage. In addition to a significant amount of carbon dioxide emission, they are a source of risk to society in both short-term (e.g., temporary city evacuation due to fire) and long-term (e.g., higher risks of landslides) cases. Consequently, the availability of tools to support local authorities in automatically identifying burned areas plays an important role in the continuous monitoring requirement to alleviate the aftereffects of such catastrophic events. The great availability of satellite acquisitions coupled with computer vision techniques represents an important step in developing such tools

Near-infrared spectroscopy study of tourniquet-induced forearm ischaemia in patients with coronary artery disease

Near-Infrared Spectroscopy (NIRS) can be employed to monitor local changes in haemodynamics and oxygenation of human tissues. A preliminary study has been performed in order to evaluate the NIRS transmittance response to induced forearm ischaemia in patients with coronary artery disease (CAD). The population consists in 40 patients with cardiovascular risk factors and angiographically documented CAD, compared to a group of 13 normal subjects. By inflating and subsequently deflating a cuff placed around the patient arm, an ischaemia has been induced and released, and the patients have been observed until recovery of the basal conditions. A custom NIRS spectrometer (IRIS) has been used to collect the backscattered light intensities from the patient forearm throughout the ischaemic and the recovery phase. The time dependence of the near-infrared transmittance on the control group is consistent with the available literature. On the contrary, the magnitude and dynamics of the NIRS signal on the CAD patients show deviations from the documented normal behavior, which can be tentatively attributed to abnormal vessel stiffness. These preliminary results, while validating the performance of the IRIS spectrometer, are strongly conducive towards the applicability of the NIRS technique to ischaemia analysis and to endothelial dysfunction characterization in CAD patients with cardiovascular risk factors

Improving Wildfire Severity Classification of Deep Learning U-Nets from Satellite Images

Uncontrolled wildfires are dangerous events capable of harming people safety. To contrast their increasing impact in recent years, a key task is an accurate detection of the affected areas and their damage assessment from satellite images. Current state-of-the-art solutions address such problem through a double convolutional neural network able to automatically detect wildfires in satellite acquisitions and associate a damage index from a defined scale. However, such deep-learning model performance is strongly dependent on many factors. In this work, we specifically focus on a key parameter, i.e., the loss function, exploited in the underlying neural networks. Besides the state-of-the-art solutions based on the Dice-MSE, among the many loss functions proposed in literature, we focus on the Binary Cross-Entropy (BCE) and the Intersection over Union (IoU), as two representatives of the distribution-based and region-based categories, respectively. Experiments show that the BCE loss function coupled with a double-step U-Net architecture provides better results than current state-of-the-art solutions on a public labeled dataset of European wildfires

Double-Step deep learning framework to improve wildfire severity classification

Wildfires are dangerous events which cause huge losses under natural, humanitarian and economical perspectives. To contrast their impact, a fast and accurate restoration can be improved through the automatic census of the event in terms of (i) delin- eation of the affected areas and (ii) estimation of damage severity, using satellite images. This work proposes to extend the state- of-the-art approach, named Double-Step U-Net (DS-UNet), able to automatically detect wildfires in satellite acquisitions and to associate a damage index from a defined scale. As a deep learning network, the DS-UNet model performance is strongly dependent on many factors. We propose to focus on alternatives in its main architecture by designing a configurable Double-Step Framework, which allows inspecting the prediction quality with different loss-functions and convolutional neural networks used as backbones. Experimental results show that the proposed framework yields better performance with up to 6.1% lower RMSE than current state of the art

A classical phenotype of Anderson-Fabry disease in a female patient with intronic mutations of the GLA gene: a case report

Background: Fabry disease (FD) is a hereditary metabolic disorder caused by the partial or total inactivation of a lysosomal hydrolase, the enzyme α-galactosidase A (GLA). This inactivation is responsible for the storage of undegraded glycosphingolipids in the lysosomes with subsequent cellular and microvascular dysfunction. The incidence of disease is estimated at 1:40,000 in the general population, although neonatal screening initiatives have found an unexpectedly high prevalence of genetic alterations, up to 1:3,100, in newborns in Italy, and have identified a surprisingly high frequency of newborn males with genetic alterations (about 1:1,500) in Taiwan. Case presentation: We describe the case of a 40-year-old female patient who presented with transient ischemic attack (TIA), discomfort in her hands, intolerance to cold and heat, severe angina and palpitations, chronic kidney disease. Clinical, biochemical and molecular studies were performed. Conclusions: Reported symptoms, peculiar findings in a renal biopsy – the evidence of occasional lamellar inclusions in podocytes and mesangial cells – and left ventricular (LV) hypertrophy, which are considered to be specific features of FD, as well as molecular evaluations, suggested the diagnosis of a classical form of FD. We detected four mutations in the GLA gene of the patient: -10C>T (g.1170C>T), c.370-77_-81del (g.7188-7192del5), c.640-16A>G (g.10115A>G), c.1000-22C>T (g.10956C>T). These mutations, located in promoter and intronic regulatory regions, have been observed in several patients with manifestations of FD. In our patient clinical picture showed a multisystemic involvement with early onset of symptoms, thus suggesting that these intronic mutations can be found even in patients with classical form of FD