Optimal Transport for Change Detection on LiDAR Point Clouds

The detection of changes occurring in multi-temporal remote sensing data plays a crucial role in monitoring several aspects of real life, such as disasters, deforestation, and urban planning. In the latter context, identifying both newly built and demolished buildings is essential to help landscape and city managers to promote sustainable development. While the use of airborne LiDAR point clouds has become widespread in urban change detection, the most common approaches require the transformation of a point cloud into a regular grid of interpolated height measurements, i.e. Digital Elevation Model (DEM). However, the DEM's interpolation step causes an information loss related to the height of the objects, affecting the detection capability of building changes, where the high resolution of LiDAR point clouds in the third dimension would be the most beneficial. Notwithstanding recent attempts to detect changes directly on point clouds using either a distance-based computation method or a semantic segmentation pre-processing step, only the M3C2 distance computation-based approach can identify both positive and negative changes, which is of paramount importance in urban planning. Motivated by the previous arguments, we introduce a principled change detection pipeline, based on optimal transport, capable of distinguishing between newly built buildings (positive changes) and demolished ones (negative changes). In this work, we propose to use unbalanced optimal transport to cope with the creation and destruction of mass related to building changes occurring in a bi-temporal pair of LiDAR point clouds. We demonstrate the efficacy of our approach on the only publicly available airborne LiDAR dataset for change detection by showing superior performance over the M3C2 and the previous optimal transport-based method presented by Nicolas Courty et al.at IGARSS 2016.Comment: Submitted to IEEE International Geoscience and Remote Sensing Symposium 2023 (IGARSS 2023

Pixel behaviour metrics for dynamic background modelling with the projected difference pattern method

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Enhanced Retention In The Passive-Avoidance Task By 5-HT1A Receptor Blockade Is Not Associated With Increased Activity Of The Central Nucleus Of The Amygdala

The effect of blockade of S-HT1A receptors was investigated on (1) retention in a mildly aversive passive-avoidance task, and (2) spontaneous single-unit activity of central nucleus of the amygdala (CeA) neurons, a brain site implicated in modulation of retention. Systemic administration of the selective S-HT1A antagonist NAN-190 immediately after training markedly-and dose-dependently-facilitated retention in the passive-avoidance task; enhanced retention was time-dependent and was not attributable to variations in wattages of shock received by animals. Systemic administration of NAN-190 had mixed effects on spontaneous single-unit activity of CeA neurons recorded extracellularly in vivo; microiontophoretic application of S-HT, in contrast, consistently and potently suppressed CeA activity. The present findings-that S-HT1A receptor blockade by NAN-190 (1) enhances retention in the passive-avoidance task, and (2) does not consistently increase spontaneous neuronal activity of the CeA-provide evidence that a serotonergic system tonically inhibits modulation of retention in the passive-avoidance task through activation of the S-HT1A receptor subtype at brain sites located outside the CeA

Scale dependant layer for self-supervised nuclei encoding

Recent developments in self-supervised learning give us the possibility to further reduce human intervention in multi-step pipelines where the focus evolves around particular objects of interest. In the present paper, the focus lays in the nuclei in histopathology images. In particular we aim at extracting cellular information in an unsupervised manner for a downstream task. As nuclei present themselves in a variety of sizes, we propose a new Scale-dependant convolutional layer to bypass scaling issues when resizing nuclei. On three nuclei datasets, we benchmark the following methods: handcrafted, pre-trained ResNet, supervised ResNet and self-supervised features. We show that the proposed convolution layer boosts performance and that this layer combined with Barlows-Twins allows for better nuclei encoding compared to the supervised paradigm in the low sample setting and outperforms all other proposed unsupervised methods. In addition, we extend the existing TNBC dataset to incorporate nuclei class annotation in order to enrich and publicly release a small sample setting dataset for nuclei segmentation and classification.Comment: 13 pages, 6 figures, 2 table

Implicit neural representation for change detection

Detecting changes that occurred in a pair of 3D airborne LiDAR point clouds, acquired at two different times over the same geographical area, is a challenging task because of unmatching spatial supports and acquisition system noise. Most recent attempts to detect changes on point clouds are based on supervised methods, which require large labelled data unavailable in real-world applications. To address these issues, we propose an unsupervised approach that comprises two components: Neural Field (NF) for continuous shape reconstruction and a Gaussian Mixture Model for categorising changes. NF offer a grid-agnostic representation to encode bi-temporal point clouds with unmatched spatial support that can be regularised to increase high-frequency details and reduce noise. The reconstructions at each timestamp are compared at arbitrary spatial scales, leading to a significant increase in detection capabilities. We apply our method to a benchmark dataset of simulated LiDAR point clouds for urban sprawling. The dataset offers different challenging scenarios with different resolutions, input modalities and noise levels, allowing a multi-scenario comparison of our method with the current state-of-the-art. We boast the previous methods on this dataset by a 10% margin in intersection over union metric. In addition, we apply our methods to a real-world scenario to identify illegal excavation (looting) of archaeological sites and confirm that they match findings from field experts.Comment: Main article is 10 pages + 3 pages of supplementary. Conference style pape

Herschel SPIRE FTS Relative Spectral Response Calibration

Herschel/SPIRE Fourier transform spectrometer (FTS) observations contain emission from both the Herschel Telescope and the SPIRE Instrument itself, both of which are typically orders of magnitude greater than the emission from the astronomical source, and must be removed in order to recover the source spectrum. The effects of the Herschel Telescope and the SPIRE Instrument are removed during data reduction using relative spectral response calibration curves and emission models. We present the evolution of the methods used to derive the relative spectral response calibration curves for the SPIRE FTS. The relationship between the calibration curves and the ultimate sensitivity of calibrated SPIRE FTS data is discussed and the results from the derivation methods are compared. These comparisons show that the latest derivation methods result in calibration curves that impart a factor of between 2 and 100 less noise to the overall error budget, which results in calibrated spectra for individual observations whose noise is reduced by a factor of 2-3, with a gain in the overall spectral sensitivity of 23% and 21% for the two detector bands, respectively.Comment: 15 pages, 13 figures, accepted for publication in Experimental Astronom

Measuring workplace bullying

Workplace bullying is increasingly being recognized as a serious problem in society today; it is also a problem that can be difficult to define and evaluate accurately. Research in this area has been hampered by lack of appropriate measurement techniques. Social scientists can play a key part in tackling the phenomenon of workplace bullying by developing and applying a range of research methods to capture its nature and incidence in a range of contexts. We review current methods of research into the phenomenon of bullying in the workplace. We examine definitional issues, including the type, frequency, and duration of bullying acts, and consider the role of values and norms of the workplace culture in influencing perception and measurement of bullying behavior. We distinguish methods that focus on: (a) inside perspectives on the experience of bullying (including questionnaires and surveys, self-report through diary-keeping, personal accounts through interviews, focus groups and critical incident technique, and projective techniques such as bubble dialogue); (b) outside perspectives (including observational methods and peer nominations); (c) multi-method approaches that integrate both inside and outside perspectives (including case studies). We suggest that multi-method approaches may offer a useful way forward for researchers and for practitioners anxious to assess and tackle the problem of bullying in their organizations.CIFPEC/CIEC - Centro de Investigação em Estudos da Criança, UM (UI 644 e 317 da FCT)