Deep Learning Solutions for TanDEM-X-based Forest Classification

In the last few years, deep learning (DL) has been successfully and massively employed in computer vision for discriminative tasks, such as image classification or object detection. This kind of problems are core to many remote sensing (RS) applications as well, though with domain-specific peculiarities. Therefore, there is a growing interest on the use of DL methods for RS tasks. Here, we consider the forest/non-forest classification problem with TanDEM-X data, and test two state-of-the-art DL models, suitably adapting them to the specific task. Our experiments confirm the great potential of DL methods for RS applications

KernSmoothIRT: An R Package for Kernel Smoothing in Item Response Theory

Item response theory (IRT) models are a class of statistical models used to describe the response behaviors of individuals to a set of items having a certain number of options. They are adopted by researchers in social science, particularly in the analysis of performance or attitudinal data, in psychology, education, medicine, marketing and other fields where the aim is to measure latent constructs. Most IRT analyses use parametric models that rely on assumptions that often are not satisfied. In such cases, a nonparametric approach might be preferable; nevertheless, there are not many software applications allowing to use that. To address this gap, this paper presents the R package KernSmoothIRT. It implements kernel smoothing for the estimation of option characteristic curves, and adds several plotting and analytical tools to evaluate the whole test/questionnaire, the items, and the subjects. In order to show the package's capabilities, two real datasets are used, one employing multiple-choice responses, and the other scaled responses

Deep Learning based data-fusion methods for remote sensing applications

In the last years, an increasing number of remote sensing sensors have been launched to orbit around the Earth, with a continuously growing production of massive data, that are useful for a large number of monitoring applications, especially for the monitoring task. Despite modern optical sensors provide rich spectral information about Earth's surface, at very high resolution, they are weather-sensitive. On the other hand, SAR images are always available also in presence of clouds and are almost weather-insensitive, as well as daynight available, but they do not provide a rich spectral information and are severely affected by speckle "noise" that make difficult the information extraction. For the above reasons it is worth and challenging to fuse data provided by different sources and/or acquired at different times, in order to leverage on their diversity and complementarity to retrieve the target information. Motivated by the success of the employment of Deep Learning methods in many image processing tasks, in this thesis it has been faced different typical remote sensing data-fusion problems by means of suitably designed Convolutional Neural Networks

Spatial attraction in migrants' settlement patterns in the city of Catania

(ProQuest: ... denotes formulae omitted.)1. IntroductionResidential location influences individuals' proximity to important resources (such as schools, hospitals, child care facilities, labor markets, and employment opportunities) and to potential risks, including environmental threats and social hazards (such as exposure to crime and violence) (Reardon 2006). Furthermore, it impacts access to social networks and other forms of social capital; overall, it shapes human interaction and the demographic processes that originate from it, such as mortality, fertility and mobility (Almquist and Butts 2012).A minority ethnic group is spatially clustered when the spatial arrangement of minority households departs from expectations based upon a random spatial allocation (Freeman, Pilger, and Alexander 1971).In broad terms, and apart from ethnic discriminatory rules enforced by law or traditions in some places and at some times, we may distinguish between two sources of spatial clustering. One source is spatial inhomogeneity or apparent contagion. Typically, the different parts of a city exhibit large variations in the price of residential property, in the accessibility of low cost public infrastructures, and in the availability of certain types of jobs; these inhomogeneities may lead to a mostly economically induced segregation. As Schelling (1971) observes, ethnicity is often correlated with income, and income with residence; so even if residential choices were unconstrained by ethnic discrimination, the different ethnic groups would not be randomly distributed among residences.The second source is spatial attraction or true contagion. Survey data on the ideal neighborhood composition for different ethnic groups in the USA, reported in Clark and Fossett (2008), show that all groups prefer living in areas where their group is a majority or near-majority. These preferences have complex origins and may reflect attachment to group identity and culture (e.g., language, religion, customs, etc.). Newly arrived minority migrants may benefit from positive spillovers in settling close to their compatriots, in terms of reciprocal acceptance, common language, and support. Transnational social networks play an important role in channeling arriving migrants into specific neighborhoods and also into particular occupations (Gelderblom and Adams 2006).However, regardless of what the basis of the individual preferences for coethnic contact is, they produce identical patterns of residential segregation (Clark and Fossett 2008). The Schelling (1971) model provides an analysis of the implications of individual preferences and shows that when a household enters a neighborhood, that neighborhood becomes more attractive to members of the household's own group and less attractive to members of other groups. In other words, the presence of a household in a given area increases the probability of others of the same group to locating nearby.It is relevant in social research to be able to distinguish between these two sources of clustering. Whereas economic induced segregation might explain some initial degree of segregation and raises questions of social equity, the Schelling model highlights the importance of individually motivated segregation and posits that even mild preferences for living with similar neighbors carry the potential of being strong determinants for residential segregation (Clark and Fossett 2008). The spatial distribution of households may be represented by a point pattern, i.e.,a set of points in a map. Ripley's K-function (Ripley 1981) is widely used to detect clustering in point processes. The inhomogeneous K-function is a version of Ripley's K-function conceived for assessing the effects of spatial attraction (or inhibition), while adjusting for the effects of spatial inhomogeneity. In other words, this approach allows us to distinguish between the two sources of clustering, by assessing clustering above and beyond that due to apparent contagion.

A CNN-based fusion method for feature extraction from sentinel data

Sensitivity to weather conditions, and specially to clouds, is a severe limiting factor to the use of optical remote sensing for Earth monitoring applications. A possible alternative is to benefit from weather-insensitive synthetic aperture radar (SAR) images. In many real-world applications, critical decisions are made based on some informative optical or radar features related to items such as water, vegetation or soil. Under cloudy conditions, however, optical-based features are not available, and they are commonly reconstructed through linear interpolation between data available at temporally-close time instants. In this work, we propose to estimate missing optical features through data fusion and deep-learning. Several sources of information are taken into account—optical sequences, SAR sequences, digital elevation model—so as to exploit both temporal and cross-sensor dependencies. Based on these data and a tiny cloud-free fraction of the target image, a compact convolutional neural network (CNN) is trained to perform the desired estimation. To validate the proposed approach, we focus on the estimation of the normalized difference vegetation index (NDVI), using coupled Sentinel-1 and Sentinel-2 time-series acquired over an agricultural region of Burkina Faso from May–November 2016. Several fusion schemes are considered, causal and non-causal, single-sensor or joint-sensor, corresponding to different operating conditions. Experimental results are very promising, showing a significant gain over baseline methods according to all performance indicators

The tangled cases of Deinogalerix (Late Miocene endemic erinaceid of Gargano) and Galericini (Eulipotyphla, Erinaceidae): A cladistic perspective

The Late Miocene giant erinaceid Deinogalerix from Scontrone and Gargano (Italy) is associated with many other vertebrates in deposits of a past island, the \u201cAbruzzo-Apulia Platform\u201d. At Gargano, Deinogalerix is accompanied by the moderately endemized Galericini Apulogalerix. This first extensive cladistic analysis is aimed at defining the relationships of Deinogalerix with characteristic members of the tribe Galericini. The analysis was performed on a matrix of 30 characters and 19 taxa and identified some smaller clades, nested within three major ones. The latter include: (i) a pentatomy of Galerix species, (ii) a polytomy of \u201ctransitional\u201d Galerix\u2013Parasorex species and (iii) a large clade with Parasorex, Schizogalerix and Gargano representatives. Galerix and Parasorex proved to be paraphyletic and Schizogalerix monophyletic. Based on the results of the analysis, Deinogalerix and Apulogalerix have distinct origins, which supports an asynchronous colonization of the island. The line of Deinogalerix possibly stemmed from some eastern species transitional between Galerix and Parasorex around Mammal Neogene (MN) zone 2. Conversely, the line of Apulogalerix originated from a primitive Parasorex ibericus, or a close relative, around MN 9\u201310. Another important result was detecting an impressive early Miocene (MN 2?) radiation of Galericini. Moreover, Schizogalerix and Parasorex originated from eastern Galericini morphologically transitional between Galerix and Parasorex

SDD: An R Package for Serial Dependence Diagrams

Detecting and measuring lag-dependencies is very important in time-series analysis. This study is commonly carried out by focusing on the linear lag-dependencies via the well-known autocorrelogram. However, in practice, there are many situations in which the autocorrelogram fails because of the nonlinear structure of the serial dependence. To cope with this problem, in this paper the R package SDD is introduced. Among the available approaches to analyze the lag-dependencies in an omnibus way, the SDD package considers the autodependogram and some of its variants. The autodependogram, defined by computing the classical Pearson χ2 -statistic at various lags, is a graphical device recently proposed in the literature to analyze lag-dependencies. The concept of reproducibility probability, and several density-based measures of divergence, are considered to define the variants of the autodependogram. An application to daily returns of the Swiss Market Index is also presented to exemplify the use of the package

On the cohomology of pro-fusion systems

We prove the Cartan-Eilenberg stable elements theorem and construct a Lyndon-Hochschild-Serre type spectral sequence for pro-fusion systems. As an application, we determine the continuous mod-$p$ cohomology ring of $\text{GL}_2(\mathbb{Z}_p)$ for any odd prime $p$.Comment: 19 pages, 4 figure