A Classification-Segmentation Framework for the Detection of Individual Trees in Dense MMS Point Cloud Data Acquired in Urban Areas

International audienceIn this paper, we present a novel framework for detecting individual trees in densely sampled 3D point cloud data acquired in urban areas. Given a 3D point cloud, the objective is to assign point-wise labels that are both class-aware and instance-aware, a task that is known as instance-level segmentation. To achieve this, our framework addresses two successive steps. The first step of our framework is given by the use of geometric features for a binary point-wise semantic classification with the objective of assigning semantic class labels to irregularly distributed 3D points, whereby the labels are defined as " tree points " and " other points ". The second step of our framework is given by a semantic segmentation with the objective of separating individual trees within the " tree points ". This is achieved by applying an efficient adaptation of the mean shift algorithm and a subsequent segment-based shape analysis relying on semantic rules to only retain plausible tree segments. We demonstrate the performance of our framework on a publicly available benchmark dataset, which has been acquired with a mobile mapping system in the city of Delft in the Netherlands. This dataset contains 10.13 M labeled 3D points among which 17.6% are labeled as " tree points ". The derived results clearly reveal a semantic classification of high accuracy (up to 90.77%) and an instance-level segmentation of high plausibility, while the simplicity, applicability and efficiency of the involved methods even allow applying the complete framework on a standard laptop computer with a reasonable processing time (less than 2.5 h)

Feature evaluation for building facade images - an empirical study

The classification of building facade images is a challenging problem that receives a great deal of attention in the photogrammetry community. Image classification is critically dependent on the features. In this paper, we perform an empirical feature evaluation task for building facade images. Feature sets we choose are basic features, color features, histogram features, Peucker features, texture features, and SIFT features. We present an approach for region-wise labeling using an efficient randomized decision forest classifier and local features. We conduct our experiments with building facade image classification on the eTRIMS dataset, where our focus is the object classes building, car, door, pavement, road, sky, vegetation, and window

Geometric Features and their Relevance for 3D Point Cloud Classification

In this paper, we focus on the automatic interpretation of 3D point cloud data in terms of associating a class label to each 3D point. While much effort has recently been spent on this research topic, little attention has been paid to the influencing factors that affect the quality of the derived classification results. For this reason, we investigate fundamental influencing factors making geometric features more or less relevant with respect to the classification task. We present a framework which consists of five components addressing point sampling, neighborhood recovery, feature extraction, classification and feature relevance assessment. To analyze the impact of the main influencing factors which are represented by the given point sampling and the selected neighborhood type, we present the results derived with different configurations of our framework for a commonly used benchmark dataset for which a reference labeling with respect to three structural classes (linear structures, planar structures and volumetric structures) as well as a reference labeling with respect to five semantic classes (Wire, Pole/Trunk, Fac¸ade, Ground and Vegetation) is available

LR-CNN : Local-aware Region CNN for vehicle detection in aerial imagery

State-of-the-art object detection approaches such as Fast/Faster R-CNN, SSD, or YOLO have difficulties detecting dense, small targets with arbitrary orientation in large aerial images. The main reason is that using interpolation to align RoI features can result in a lack of accuracy or even loss of location information. We present the Local-aware Region Convolutional Neural Network (LR-CNN), a novel two-stage approach for vehicle detection in aerial imagery. We enhance translation invariance to detect dense vehicles and address the boundary quantization issue amongst dense vehicles by aggregating the high-precision RoIs' features. Moreover, we resample high-level semantic pooled features, making them regain location information from the features of a shallower convolutional block. This strengthens the local feature invariance for the resampled features and enables detecting vehicles in an arbitrary orientation. The local feature invariance enhances the learning ability of the focal loss function, and the focal loss further helps to focus on the hard examples. Taken together, our method better addresses the challenges of aerial imagery. We evaluate our approach on several challenging datasets (VEDAI, DOTA), demonstrating a significant improvement over state-of-the-art methods. We demonstrate the good generalization ability of our approach on the DLR 3K dataset. © 2020 Copernicus GmbH. All rights reserved

William Pitt and the origins of the loyalist association movement of 1792

© 1996 Cambridge University Press.This article presents new and conclusive evidence to resolve the long-running controversy over whether the loyalist association movement of 1792 was spontaneous or was crafted by government. It shows that Pitt and his colleagues did not know in advance of John Reeves's proposals for the Crown and Anchor association before they were published on 23 November and it suggests who Reeves's original collaborators probably were. It then goes on to show how Pitt and his cousin, Lord Grenville, confronted with many demands and proposals for associations at this time, quickly seized upon the Reeves project as the most adaptable to their own ends and produced a new draft, redefining his proposals in the directions they were prepared to see such a movement take. This they induced Reeves to publish as a second declaration on 26 November and they went on to promote as the example and inspiration for a wider association movement

Genomic Hotspots for Adaptation: The Population Genetics of Müllerian Mimicry in the Heliconius melpomene Clade

Wing patterning in Heliconius butterflies is a longstanding example of both Müllerian mimicry and phenotypic radiation under strong natural selection. The loci controlling such patterns are “hotspots” for adaptive evolution with great allelic diversity across different species in the genus. We characterise nucleotide variation, genotype-by-phenotype associations, linkage disequilibrium, and candidate gene expression at two loci and across multiple hybrid zones in Heliconius melpomene and relatives. Alleles at HmB control the presence or absence of the red forewing band, while alleles at HmYb control the yellow hindwing bar. Across HmYb two regions, separated by ∼100 kb, show significant genotype-by-phenotype associations that are replicated across independent hybrid zones. In contrast, at HmB a single peak of association indicates the likely position of functional sites at three genes, encoding a kinesin, a G-protein coupled receptor, and an mRNA splicing factor. At both HmYb and HmB there is evidence for enhanced linkage disequilibrium (LD) between associated sites separated by up to 14 kb, suggesting that multiple sites are under selection. However, there was no evidence for reduced variation or deviations from neutrality that might indicate a recent selective sweep, consistent with these alleles being relatively old. Of the three genes showing an association with the HmB locus, the kinesin shows differences in wing disc expression between races that are replicated in the co-mimic, Heliconius erato, providing striking evidence for parallel changes in gene expression between Müllerian co-mimics. Wing patterning loci in Heliconius melpomene therefore show a haplotype structure maintained by selection, but no evidence for a recent selective sweep. The complex genetic pattern contrasts with the simple genetic basis of many adaptive traits studied previously, but may provide a better model for most adaptation in natural populations that has arisen over millions rather than tens of years

Phylogenetic Codivergence Supports Coevolution of Mimetic Heliconius Butterflies

The unpalatable and warning-patterned butterflies _Heliconius erato_ and _Heliconius melpomene_ provide the best studied example of mutualistic Müllerian mimicry, thought – but rarely demonstrated – to promote coevolution. Some of the strongest available evidence for coevolution comes from phylogenetic codivergence, the parallel divergence of ecologically associated lineages. Early evolutionary reconstructions suggested codivergence between mimetic populations of _H. erato_ and _H. melpomene_, and this was initially hailed as the most striking known case of coevolution. However, subsequent molecular phylogenetic analyses found discrepancies in phylogenetic branching patterns and timing (topological and temporal incongruence) that argued against codivergence. We present the first explicit cophylogenetic test of codivergence between mimetic populations of _H. erato_ and _H. melpomene_, and re-examine the timing of these radiations. We find statistically significant topological congruence between multilocus coalescent population phylogenies of _H. erato_ and _H. melpomene_, supporting repeated codivergence of mimetic populations. Divergence time estimates, based on a Bayesian coalescent model, suggest that the evolutionary radiations of _H. erato_ and _H. melpomene_ occurred over the same time period, and are compatible with a series of temporally congruent codivergence events. This evidence supports a history of reciprocal coevolution between Müllerian co-mimics characterised by phylogenetic codivergence and parallel phenotypic change

The In Situ Signature of Cyclotron Resonant Heating

The dissipation of magnetized turbulence is an important paradigm for describing heating and energy transfer in astrophysical environments such as the solar corona and wind; however, the specific collisionless processes behind dissipation and heating remain relatively unconstrained by measurements. Remote sensing observations have suggested the presence of strong temperature anisotropy in the solar corona consistent with cyclotron resonant heating. In the solar wind, in situ magnetic field measurements reveal the presence of cyclotron waves, while measured ion velocity distribution functions have hinted at the active presence of cyclotron resonance. Here, we present Parker Solar Probe observations that connect the presence of ion-cyclotron waves directly to signatures of resonant damping in observed proton-velocity distributions. We show that the observed cyclotron wave population coincides with both flattening in the phase space distribution predicted by resonant quasilinear diffusion and steepening in the turbulent spectra at the ion-cyclotron resonant scale. In measured velocity distribution functions where cyclotron resonant flattening is weaker, the distributions are nearly uniformly subject to ion-cyclotron wave damping rather than emission, indicating that the distributions can damp the observed wave population. These results are consistent with active cyclotron heating in the solar wind

Loss of LGR4/GPR48 causes severe neonatal salt-wasting due to disrupted WNT signaling altering adrenal zonation.

Disorders of isolated mineralocorticoid deficiency causing potentially life-threatening salt-wasting crisis early in life have been associated with gene variants of aldosterone biosynthesis or resistance, but in some patients no such variants are found. WNT/β-catenin signaling is crucial for differentiation and maintenance of the aldosterone producing adrenal zona glomerulosa (zG). We describe a highly consanguineous family with multiple perinatal deaths or infants presenting at birth with failure to thrive, severe salt-wasting crises associated with isolated hypoaldosteronism, nail anomalies, short stature, and deafness. Whole exome sequencing revealed a homozygous splice variant in the R-SPONDIN receptor LGR4 gene (c.618-1G>C) regulating WNT signaling. The resulting transcripts affected protein function and stability, and resulted in loss of Wnt/β-catenin signaling in vitro. The impact of LGR4 inactivation was analyzed by adrenal cortex specific ablation of Lgr4, using Lgr4Flox/Flox mated with Sf1:Cre mice. Inactivation of Lgr4 within the adrenal cortex in the mouse model caused decreased WNT signaling, aberrant zonation with deficient zG and reduced aldosterone production. Thus, human LGR4 mutations establish a direct link between LGR4 inactivation and decreased canonical WNT signaling with abnormal zG differentiation and endocrine function. Therefore, variants in WNT signaling and its regulators should systematically be considered in familial hyperreninemic hypoaldosteronism