Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers

Scene parsing, or semantic segmentation, consists in labeling each pixel in an image with the category of the object it belongs to. It is a challenging task that involves the simultaneous detection, segmentation and recognition of all the objects in the image. The scene parsing method proposed here starts by computing a tree of segments from a graph of pixel dissimilarities. Simultaneously, a set of dense feature vectors is computed which encodes regions of multiple sizes centered on each pixel. The feature extractor is a multiscale convolutional network trained from raw pixels. The feature vectors associated with the segments covered by each node in the tree are aggregated and fed to a classifier which produces an estimate of the distribution of object categories contained in the segment. A subset of tree nodes that cover the image are then selected so as to maximize the average "purity" of the class distributions, hence maximizing the overall likelihood that each segment will contain a single object. The convolutional network feature extractor is trained end-to-end from raw pixels, alleviating the need for engineered features. After training, the system is parameter free. The system yields record accuracies on the Stanford Background Dataset (8 classes), the Sift Flow Dataset (33 classes) and the Barcelona Dataset (170 classes) while being an order of magnitude faster than competing approaches, producing a 320 \times 240 image labeling in less than 1 second.Comment: 9 pages, 4 figures - Published in 29th International Conference on Machine Learning (ICML 2012), Jun 2012, Edinburgh, United Kingdo

Estimation of species relative abundances and habitat preferences using opportunistic data

We develop a new statistical procedure to monitor, with opportunist data, relative species abundances and their respective preferences for dierent habitat types. Following Giraud et al. (2015), we combine the opportunistic data with some standardized data in order to correct the bias inherent to the opportunistic data collection. Our main contributions are (i) to tackle the bias induced by habitat selection behaviors, (ii) to handle data where the habitat type associated to each observation is unknown, (iii) to estimate probabilities of selection of habitat for the species. As an illustration, we estimate common bird species habitat preferences and abundances in the region of Aquitaine (France)

Indoor Semantic Segmentation using depth information

This work addresses multi-class segmentation of indoor scenes with RGB-D inputs. While this area of research has gained much attention recently, most works still rely on hand-crafted features. In contrast, we apply a multiscale convolutional network to learn features directly from the images and the depth information. We obtain state-of-the-art on the NYU-v2 depth dataset with an accuracy of 64.5%. We illustrate the labeling of indoor scenes in videos sequences that could be processed in real-time using appropriate hardware such as an FPGA.Comment: 8 pages, 3 figure

Aimi Antonio, Krzysztof Makowski et Emilia Perassi, Lambayeque. Nuevos horizontes de la arqueología peruana

L’ouvrage Lambayeque. Nuevos horizontes de la arqueología peruana, édité par A. Aimi, K. Makowski et E. Perassi, grâce au Proyecto ProPomac de l’Università degli Studi di Milano et au Fond italo-péruvien, est une monographie détaillée des dernières recherches historiographiques, iconographiques, ethnohistoriques et archéologiques consacrées à la culture lambayeque, qui fleurit sur la côte nord du Pérou à partir du ixe siècle apr. J.-C. Ce livre,..

Convolutional Nets and Watershed Cuts for Real-Time Semantic Labeling of RGBD Videos

International audienceThis work addresses multi-class segmentation of indoor scenes with RGB-D inputs. While this area of research has gained much attention recently, most works still rely on handcrafted features. In contrast, we apply a multiscale convolutional network to learn features directly from the images and the depth information. Using a frame by frame labeling, we obtain nearly state-of-the-art performance on the NYU-v2 depth dataset with an accuracy of 64.5%. We then show that the labeling can be further improved by exploiting the temporal consistency in the video sequence of the scene. To that goal, we present a method producing temporally consistent superpixels from a streaming video. Among the di erent methods producing superpixel segmentations of an image, the graph-based approach of Felzenszwalb and Huttenlocher is broadly employed. One of its interesting properties is that the regions are computed in a greedy manner in quasi-linear time by using a minimum spanning tree. In a framework exploiting minimum spanning trees all along, we propose an efficient video segmentation approach that computes temporally consistent pixels in a causal manner, filling the need for causal and real-time applications. We illustrate the labeling of indoor scenes in video sequences that could be processed in real-time using appropriate hardware such as an FPGA

Capitalising on Opportunistic Data for Monitoring Species Relative Abundances

With the internet, a massive amount of information on species abundance can be collected under citizen science programs. However, these data are often difficult to use directly in statistical inference, as their collection is generally opportunistic, and the distribution of the sampling effort is often not known. In this paper, we develop a general statistical framework to combine such ``opportunistic data'' with data collected using schemes characterized by a known sampling effort. Under some structural assumptions regarding the sampling effort and detectability, our approach allows to estimate the relative abundance of several species in different sites. It can be implemented through a simple generalized linear model. We illustrate the framework with typical bird datasets from the Aquitaine region, south-western France. We show that, under some assumptions, our approach provides estimates that are more precise than the ones obtained from the dataset with a known sampling effort alone. When the opportunistic data are abundant, the gain in precision may be considerable, especially for the rare species. We also show that estimates can be obtained even for species recorded only in the opportunistic scheme. Opportunistic data combined with a relatively small amount of data collected with a known effort may thus provide access to accurate and precise estimates of quantitative changes in relative abundance over space and/or time

Préserver les espaces agricoles périurbains face à l’étalement urbain. Une problématique locale ?

Le devenir des espaces agricoles face à l’étalement urbain est étudié en croisant trois points de vue. Premièrement, nous caractérisons l’étalement urbain consommateur d’espaces agricoles dans deux régions sud européennes : le Languedoc-Roussillon en France et le Nord-Ouest du Portugal. Deuxièmement, nous recensons les documents, règlements et outils de politiques publiques mis à la disposition des acteurs des deux régions d’étude pour réagir à cet étalement urbain en œuvrant pour le développement durable. Troisièmement, nous faisons un zoom sur la mise en œuvre de deux projets locaux contrastés, l’un règlementaire, un Schéma de Cohérence Territoriale (SCoT), en France, et l’autre d’orientation, un Agenda 21 local, au Portugal. Nos résultats montrent que l’étalement urbain se poursuit même s’il ralentit selon les derniers chiffres. Et les nouveaux outils et documents générés par les politiques publiques pour l’endiguer et pour protéger les espaces agricoles, restent peu mobilisés localement. Nous en concluons que la gestion de l’étalement urbain se joue dans les multiples articulations entre le local et les processus englobants.The future of agricultural areas in regards to urban sprawl is studied in three steps. Firstly, we analyse the encroachment of urban sprawl onto agricultural land in two regions of Southern Europe through GIS tools : Languedoc-Roussillon in France and the North-West of Portugal. Secondly, we survey documents, laws and tools of public policy available to local planning actors to react to urban sprawl and/or maintain peri-urban agricultural land, thus contributing to sustainable development. Finally, we focus on two different local projects, one normative, a Territorial Coherence Plan (SCoT) (in France) and the other non-normative, Local Agenda 21 (in Portugal). As a result, we show that urban sprawl continues even if recent data reports a decrease. The new tools and documents generated by public policy to confine urban sprawl and protect agricultural land continue to be rarely used locally. We conclude that urban sprawl can be managed in multiple articulations between the local and global scales

Wage Incidence of a Large Corporate Tax Credit: Contrasting Employee - and Firm - Level Evidence

The present paper sheds new light on the incidence of firm taxation by exploiting the design of a large-scale corporate income tax credit in France. The tax credit is proportional to the wage bill of workers paid below a hourly wage threshold, which induces a discontinuity in mandatory levies at the employee level. We use discontinuities at the employee level in order to estimate firm-level incidence. This turns out to be the relevant level for the effects of the policy, which would be undetectable with an estimation focused on the employee level impact of the shock. Relying on exhaustive matched employer-employee data, we find a discrepancy between the absence of incidence at the employee level and a substantial incidence on wages at the firm level, around 50%. We find more over that the policy in question has stark (anti)-redistributive effects. The tax cut is targeting the lowest part of wage earners, but the benefits accrue to other employees inside the firm, who earn substantially higher wages on average

Évaluation interdisciplinaire des impacts du CICE en matière d’emplois et de salaires:Rapport du Laboratoire Interdisciplinaire d’Évaluation des Politiques Publiques (LIEPP) de Sciences Po en réponse à l’appel à évaluation de France Stratégie

Le crédit d’impôt pour la compétitivité et l’emploi (CICE) a été institué avec l’objectif d’améliorer la compétitivité des entreprises. Pour étudier ses différents effets potentiels sur l’emploi et les salaires, l’évaluation présentée ici s’appuie d’une part sur une analyse économique, et d’autre part sur une étude sociologique, dont les résultats qualitatifs avaient été détaillés lors du rapport remis le 29 septembre 2016 par le LIEPP de Sciences Po à France Stratégie. L’analyse micro-économique basée sur les données fiscales et sociales des entreprises fait tout d’abord apparaître que, au niveau de l’emploi, les décisions des nouvelles embauches des entreprises n’ont pas été affectées par la présence de la nette discontinuité du CICE. De plus, comparativement aux entreprises moins intensément ciblées par la mesure, les entreprises les plus intensément ciblées par le CICE n’ont pas connu de hausse de l’emploi entre 2013 et 2015, et cela quelle que soit la catégorie socio-professionnelle. Concernant l’effet sur les salaires, notre analyse montre que la mesure n’a pas eu d’impact détectable sur la distribution des hausses de salaires mais il apparaît toutefois qu’au niveau de l’entreprise, les sommes allouées dans le cadre du CICE ont été en partie reversées aux salariés, sous forme de hausses de salaires, en particulier aux cadres, professions intellectuelles supérieures et professions intermédiaires. Il faut garder à l’esprit que les conclusions de cette évaluation ne portent que sur les trois premières années de mise en place du CICE, les données de 2016 et 2017 n’étant pas encore disponibles au moment de l’évaluation. Il convient enfin de préciser la difficulté de toute étude portant sur le CICE : celui-ci n’a pas été conçu de manière à être évalué par un dispositif expérimental