Land Parcel Identification System (LPIS) Anomalies' Sampling and Spatial Pattern: Towards convergence of ecological methodologies and GIS technologies

To date, the Land Parcel Identification System (LPIS) has often been proposed as the foundation for effective spatial management of agriculture and the environment and many land managers have suggested incorporating it in most of the instruments for sustainable agriculture. The LPIS is originally used for registration of agricultural reference parcels considered eligible for annual payments of European Common Agricultural Policy (CAP) subsidies to farmers. Its intrinsic quality depends on the frequency and magnitude of the discrepancies in area, since some parcels can be under- or over-declared by farmers compared with reference registered within the LPIS. General application of the LPIS therefore depends on our capacity to ¿ first identify and explain the causes of these area discrepancies perceived as anomalies by national CAP payment agencies ¿second, to propose future improvements in its overall quality. From a set of images used during the 2005 Control with Remote Sensing (CwRS) campaign, using the geographic information system (GIS) and ecological methodologies we assessed the quality of the LPIS by identifying the diversity of the existing anomalies. To that end, the ecological sampling method was adapted to the specific case of image-based detection of anomalies. The observed anomalies assemblages obtained from a set of European Member States representing the four types of LPIS were analysed to establish the spatial pattern of the anomalies. We showed that the twelve zones surveyed can be grouped into four different clusters, each individually correlated with the presence of certain categories of LPIS anomaly. Some clusters were more particularly related to the presence of natural and anthropogenic landscape features, whereas others were typified by anomalies which stemmed from the process for creating and updating the LPIS, which accounted for 20% of the anomalies detected. Finally, we also showed that, even if useful for establishing procedures to manage the LPIS, the LPIS typology used in the European Union had no effect on the anomalies assemblage or on the spatial pattern; consequently, the type of LPIS no longer needs to be considered and LPIS anomalies assemblages could be pooled across Europe. In the light of the results obtained, different proposals are made to improve LPIS quality by: ¿ identifying the critical points along the LPIS management chain; ¿ using landscape ecological methodologies to explain the causes of the clusters observed; and ¿ extrapolating the whole results in the CwRS risk analysis to perform ex-ante LPIS anomalies risk map. Keywords: Land Parcel Identification System, Control with Remote Sensing, orthophoto, quality assessment, diversity, spatial pattern, landscape structureJRC.G.3-Agricultur

Anomaly Detection in Autonomous Driving: A Survey

Nowadays, there are outstanding strides towards a future with autonomous vehicles on our roads. While the perception of autonomous vehicles performs well under closed-set conditions, they still struggle to handle the unexpected. This survey provides an extensive overview of anomaly detection techniques based on camera, lidar, radar, multimodal and abstract object level data. We provide a systematization including detection approach, corner case level, ability for an online application, and further attributes. We outline the state-of-the-art and point out current research gaps.Comment: Daniel Bogdoll and Maximilian Nitsche contributed equally. Accepted for publication at CVPR 2022 WAD worksho

Expansion rate & dispersal pattern of the non-native Roesel’s bush-cricket in Sweden

Environmental change and anthropogenic activities influence species distributions. Species introductions have become increasingly common in an era of globalization and increased international trade and travel. The establishment of introduced species outside their native range and subsequent spread are of great conservation concern. Introduced species that become invasive, spread rapidly and reach high abundance, may cause the extinction of native species, disrupt ecosystem functioning and pose a threat to human health and the economy. It is therefore of great interest to understand the processes and mechanisms involved in species range expansion in order to develop effective management strategies. In this thesis I examine the influence of the landscape on species’ distribution and analyse patterns of range expansion of a non-native insect in south-central Sweden. Roesel’s bush-cricket (Metrioptera roeselii) was chosen as a model organism as its biology is well studied and its range expansion has been documented not only in Sweden but also in several other European countries. The aims of this thesis were (I) to identify landscape variables that predict the species distribution, (II) to estimate the rate of range expansion, (III) to identify the source of range expansion in south-central Sweden and to assess the dispersal pattern using population genetic data, and (IV) to analyse the influence of landscape composition and structure on population connectivity. I analysed species distribution, genetic and landscape data using a range of statistical modelling techniques in combination with geographic information systems (GIS). The results showed that the amounts of arable land, pasture and rural settlements as well as linear habitat elements are important predictors of the species’ distribution. During the last three decades, Metrioptera roeselii has expanded its range from the northern shores of the Lake Mälaren at an estimated rate of 0.3 - 3.16 km/year. The genetic diversity across the range was surprisingly high and degree of population differentiation was low to moderate likely due to frequent gene flow between populations in the centre of the species range and decreased gene flow towards the range margin. It appears the species establishes populations through infrequent long-distance and frequent short-distance dispersal (natural, human-mediated)

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection

Time series are the primary data type used to record dynamic system measurements and generated in great volume by both physical sensors and online processes (virtual sensors). Time series analytics is therefore crucial to unlocking the wealth of information implicit in available data. With the recent advancements in graph neural networks (GNNs), there has been a surge in GNN-based approaches for time series analysis. Approaches can explicitly model inter-temporal and inter-variable relationships, which traditional and other deep neural network-based methods struggle to do. In this survey, we provide a comprehensive review of graph neural networks for time series analysis (GNN4TS), encompassing four fundamental dimensions: Forecasting, classification, anomaly detection, and imputation. Our aim is to guide designers and practitioners to understand, build applications, and advance research of GNN4TS. At first, we provide a comprehensive task-oriented taxonomy of GNN4TS. Then, we present and discuss representative research works and, finally, discuss mainstream applications of GNN4TS. A comprehensive discussion of potential future research directions completes the survey. This survey, for the first time, brings together a vast array of knowledge on GNN-based time series research, highlighting both the foundations, practical applications, and opportunities of graph neural networks for time series analysis.Comment: 27 pages, 6 figures, 5 table

Characterizing zebra crossing zones using LiDAR data

Light detection and ranging (LiDAR) scanning in urban environments leads to accurate and dense three-dimensional point clouds where the different elements in the scene can be precisely characterized. In this paper, two LiDAR-based algorithms that complement each other are proposed. The first one is a novel profiling method robust to noise and obstacles. It accurately characterizes the curvature, the slope, the height of the sidewalks, obstacles, and defects such as potholes. It was effective for 48 of 49 detected zebra crossings, even in the presence of pedestrians or vehicles in the crossing zone. The second one is a detailed quantitative summary of the state of the zebra crossing. It contains information about the location, the geometry, and the road marking. Coarse grain statistics are more prone to obstacle-related errors and are only fully reliable for 18 zebra crossings free from significant obstacles. However, all the anomalous statistics can be analyzed by looking at the associated profiles. The results can help in the maintenance of urban roads. More specifically, they can be used to improve the quality and safety of pedestrian routesConsellería de Cultura, Educación e Ordenación Universitaria, Grant/Award Numbers: accreditation 2019-2022 ED431G-2019/04, 2022-2024, ED431C2022/16, ED481A-2020/231; European Regional Development Fund (ERDF); CiTIUS-Research Center in Intelligent Technologies of the University of Santiago de Compostela as a Research Center of the Galician University System; Ministry of Economy and Competitiveness, Government of Spain, Grant/Award Number: PID2019-104834GB-I00; National Department of Traffic (DGT) through the project Analysis of Indicators Big-Geodata on Urban Roads for the Dynamic Design of Safe School Roads, Grant/Award Number: SPIP2017-02340S

Imagining counterfactual worlds in autism spectrum disorder

Two experiments are presented which explore online counterfactual processing in autism spectrum disorder (ASD) using eye-tracking. Participants’ eye movements were tracked while they read factual and counterfactual sentences in an anomaly detection task. In Experiment 1, the sentences depicted everyday counterfactual situations (e.g. If Joanne had remembered her umbrella, her hair would have been dry/wet when she arrived home). Sentences in Experiment 2 depicted counterfactual versions of real world events (e.g. If the Titanic had not hit an iceberg, it would have survived/sunk along with all the passengers). Results from both experiments suggest that counterfactual understanding is undiminished in adults with ASD. In fact, participants with ASD were faster than TD participants to detect anomalies within realistic, discourse-based counterfactuals (Experiment 1). Detection was comparable for TD and ASD groups when understanding could be grounded in knowledge about reality (Experiment 2), though the two groups employed subtly different strategies for responding to and recovering from counterfactual inconsistent words. These data argue against general difficulties in global coherence and complex integration in ASD