A Graph-Cut-Based Method for Spatio-Temporal Segmentation of Fire from Satellite Observations

International audienceWe propose a new method based on graph cuts for the segmentation of burned areas in time series of satellite images. The method consists in rewriting a segmentation problem as a (s, t)-min-cut on the spatio-temporal image graph and computing this minimal cut. As burned areas grow in time, we introduce growth constraint in graph cuts by using directed infinite links connecting pixels at the same spatial locations in successive image frames. This method guarantees to find the globally optimal segmentation satisfying the growth constraint in small time complexity. Experimental results on a set of MODIS measurements over the Northern Australia demonstrated that the new approach succeeded in combining both spatial and temporal information for accurate segmentation of burned areas

Spatio-Temporal Video Segmentation with Shape Growth or Shrinkage Constraint

We propose a new method for joint segmentation of monotonously growing or shrinking shapes in a time sequence of noisy images. The task of segmenting the image time series is expressed as an optimization problem using the spatio-temporal graph of pixels, in which we are able to impose the constraint of shape growth or of shrinkage by introducing monodirectional infinite links connecting pixels at the same spatial locations in successive image frames. The globally optimal solution is computed with a graph cut. The performance of the proposed method is validated on three applications: segmentation of melting sea ice floes and of growing burned areas from time series of 2D satellite images, and segmentation of a growing brain tumor from sequences of 3D medical scans. In the latter application, we impose an additional intersequences inclusion constraint by adding directed infinite links between pixels of dependent image structures

From Pillars to AI Technology-Based Forest Fire Protection Systems

The importance of forest environment in the perspective of the biodiversity as well as from the economic resources which forests enclose, is more than evident. Any threat posed to this critical component of the environment should be identified and attacked through the use of the most efficient available technological means. Early warning and immediate response to a fire event are critical in avoiding great environmental damages. Fire risk assessment, reliable detection and localization of fire as well as motion planning, constitute the most vital ingredients of a fire protection system. In this chapter, we review the evolution of the forest fire protection systems and emphasize on open issues and the improvements that can be achieved using artificial intelligence technology. We start our tour from the pillars which were for a long time period, the only possible method to oversee the forest fires. Then, we will proceed to the exploration of early AI systems and will end-up with nowadays systems that might receive multimodal data from satellites, optical and thermal sensors, smart phones and UAVs and use techniques that cover the spectrum from early signal processing algorithms to latest deep learning-based ones to achieving the ultimate goal

Intelligent Data Analytics using Deep Learning for Data Science

Nowadays, data science stimulates the interest of academics and practitioners because it can assist in the extraction of significant insights from massive amounts of data. From the years 2018 through 2025, the Global Datasphere is expected to rise from 33 Zettabytes to 175 Zettabytes, according to the International Data Corporation. This dissertation proposes an intelligent data analytics framework that uses deep learning to tackle several difficulties when implementing a data science application. These difficulties include dealing with high inter-class similarity, the availability and quality of hand-labeled data, and designing a feasible approach for modeling significant correlations in features gathered from various data sources. The proposed intelligent data analytics framework employs a novel strategy for improving data representation learning by incorporating supplemental data from various sources and structures. First, the research presents a multi-source fusion approach that utilizes confident learning techniques to improve the data quality from many noisy sources. Meta-learning methods based on advanced techniques such as the mixture of experts and differential evolution combine the predictive capacity of individual learners with a gating mechanism, ensuring that only the most trustworthy features or predictions are integrated to train the model. Then, a Multi-Level Convolutional Fusion is presented to train a model on the correspondence between local-global deep feature interactions to identify easily confused samples of different classes. The convolutional fusion is further enhanced with the power of Graph Transformers, aggregating the relevant neighboring features in graph-based input data structures and achieving state-of-the-art performance on a large-scale building damage dataset. Finally, weakly-supervised strategies, noise regularization, and label propagation are proposed to train a model on sparse input labeled data, ensuring the model\u27s robustness to errors and supporting the automatic expansion of the training set. The suggested approaches outperformed competing strategies in effectively training a model on a large-scale dataset of 500k photos, with just about 7% of the images annotated by a human. The proposed framework\u27s capabilities have benefited various data science applications, including fluid dynamics, geometric morphometrics, building damage classification from satellite pictures, disaster scene description, and storm-surge visualization

Spatial Analysis for Landscape Changes

Recent increasing trends of the occurrence of natural and anthropic processes have a strong impact on landscape modification, and there is a growing need for the implementation of effective instruments, tools, and approaches to understand and manage landscape changes. A great improvement in the availability of high-resolution DEMs, GIS tools, and algorithms of automatic extraction of landform features and change detections has favored an increase in the analysis of landscape changes, which became an essential instrument for the quantitative evaluation of landscape changes in many research fields. One of the most effective ways of investigating natural landscape changes is the geomorphological one, which benefits from recent advances in the development of digital elevation model (DEM) comparison software and algorithms, image change detection, and landscape evolution models. This Special Issue collects six papers concerning the application of traditional and innovative multidisciplinary methods in several application fields, such as geomorphology, urban and territorial systems, vegetation restoration, and soil science. The papers include multidisciplinary studies that highlight the usefulness of quantitative analyses of satellite images and UAV-based DEMs, the application of Landscape Evolution Models (LEMs) and automatic landform classification algorithms to solve multidisciplinary issues of landscape changes. A review article is also presented, dealing with the bibliometric analysis of the research topic

Visualizing Spatio-Temporal data

The amount of spatio-temporal data produced everyday has sky rocketed in the recent years due to the commercial GPS systems and smart devices. Together with this, the need for tools and techniques to analyze this kind of data have also increased. A major task of spatio-temporal data analysis is to discover relationships and patterns among spatially and temporally scattered events. However, most of the existing visualization techniques implement a top-down approach i.e, they require prior knowledge of existing patterns. In this dissertation, I present my novel visualization technique called Storygraph which supports bottom-up discovery of patterns. Since Storygraph presents and integrated view, analysis of events can be done with losing either of time or spatial contexts. In addition, Storygraph can handle spatio-temporal uncertainty making it ideal for data being extracted from text. In the subsequent chapters, I demonstrate the versatility and the effectiveness of the Storygraph along with case studies from my published works. Finally, I also talk about edge bundling in Storygraph to enhance the aesthetics and improve the readability of Storygraph

Fire Data as Proxy for Anthropogenic Landscape Change in the Yucatán

abstract: Fire is one of the earliest and most common tools used by humans to modify the earth surface. Landscapes in the Yucatán Peninsula are composed of a mosaic of old growth subtropical forest, secondary vegetation, grasslands, and agricultural land that represent a well-documented example of anthropogenic intervention, much of which involves the use of fire. This research characterizes land use systems and land cover changes in the Yucatán during the 2000–2010 time period. We used an active fire remotely sensed data time series from the Moderate Resolution Imaging Spectroradiometer (MODIS), in combination with forest loss, and anthrome map sources to (1) establish the association between fire and land use change in the region; and (2) explore links between the spatial and temporal patterns of fire and specific types of land use practices, including within- and between-anthromes variability. A spatial multinomial logit model was constructed using fire, landscape configuration, and a set of commonly used control variables to estimate forest persistence, non-forest persistence, and change. Cross-tabulations and descriptive statistics were used to explore the relationships between fire occurrence, location, and timing with respect to the geography of land use. We also compared fire frequencies within and between anthrome groups using a negative binomial model and Tukey pairwise comparisons. Results show that fire data broadly reproduce the geography and timing of anthropogenic land change. Findings indicate that fire and landscape configuration is useful in explaining forest change and non-forest persistence, especially in fragmented (mosaicked) landscapes. Absence of fire occurrence is related usefully to the persistence of spatially continuous core areas of older growth forest. Fire has a positive relationship with forest to non-forest change and a negative relationship with forest persistence. Fire is also a good indicator to distinguish between anthrome groups (e.g., croplands and villages). Our study suggests that active fire data series are a reasonable proxy for anthropogenic land persistence/change in the context of the Yucatán and are useful to differentiate quantitatively and qualitatively between and within anthromes