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Detailed information about seismic building structural types (SBSTs) is crucial for accurate earthquake vulnerability and risk modeling as it reflects the main load-bearing structures of buildings and, thus, the behavior under seismic load. However, for numerous urban areas in earthquake prone regions this information is mostly outdated, unavailable, or simply not existent. To this purpose, we present an effective approach to estimate SBSTs by combining scarce in situ observations, multi-sensor remote sensing data and machine learning techniques. In particular, an approach is introduced, which deploys a sequential procedure comprising five main steps, namely calculation of features from remote sensing data, feature selection, outlier detection, generation of synthetic samples, and supervised classification under consideration of both Support Vector Machines and Random Forests. Experimental results obtained for a representative study area, including large parts of the city of Padang (Indonesia), assess the capabilities of the presented approach and confirm its great potential for a reliable area-wide estimation of SBSTs and an effective earthquake loss modeling based on remote sensing, which should be further explored in future research activities.

Purpose -Theories of organizational learning and sustainability must be able to respond to contemporary social issues and accommodate, in some way, the multiplicity of perspectives that are present in society on these topics. One way of developing multi-perspectival capacities in the scientific understandings is through the building of metatheory. Nowhere is this task more urgently needed than in the study of organisational sustainability. To be sustainable, organisations must not only meet economic, environmental, social and governance requirements but also learn to embody them in their practices and values even during times of turbulence and extraordinary upheaval. The purpose of this paper is to propose a metatheoretical approach to organizational sustainability that can accommodate this plurality. Design/methodology/approach -Three important metatheoretical lenses -the developmental, internal-external and learning lenses -are presented which have particular relevance to turbulent organizational environments and the transformational imperatives that arise from them. These lenses are then used individually and in combination to discuss several paradoxes related to learning and sustainability issues. Findings -The growth, learning and sustainability paradoxes present a number of challenges to organisational learning capacities that can be usefully discussed within a metatheoretical context. The set of metatheoretical lenses identified here provide some new avenues for achieving authentic sustainability. Practical implications -There are two important implications of metatheoretical discussion. The first is the opening up of new directions for middle-range theory. The second is the capacity of metatheory to critically examine extant theories and research paradigms. Several issues are raised in this paper concerning the evaluation of current theories of organisational learning and sustainability. Originality/value -The metatheoretical approach to learning and sustainability proposed here resolves some fundamental paradoxes facing organisations and it opens up new ways of conceptualising the radical transformations required to meet the sustainability challenges that are being faced in the twenty-first century.

Background: Distributional responses by alpine taxa to repeated, glacial-interglacial cycles throughout the last two million years have signi cantly in uenced the spatial genetic structure of populations. These effects have been exacerbated for the American pika (Ochotona princeps), a small alpine lagomorph constrained by thermal sensitivity and a limited dispersal capacity. As a species of conservation concern, long-term lack of gene ow has important consequences for landscape genetic structure and levels of diversity within populations. Here, we use reduced representation sequencing (ddRADseq) to provide a genome-wide perspective on patterns of genetic variation across pika populations representing distinct subspecies. To investigate how landscape and environmental features shape genetic variation, we collected genetic samples from distinct geographic regions as well as across ner spatial scales in two geographically proximate mountain ranges of eastern Nevada. Results: Our genome-wide analyses corroborate range-wide, mitochondrial subspeci c designations and reveal pronounced ne-scale population structure between the Ruby Mountains and East Humboldt Range of eastern Nevada. Populations in Nevada were characterized by low genetic diversity (=0.0006-0.0009; W =0.0005-0.0007) relative to populations in California (=0.0014-0.0019; W =0.0011-0.0017) and the Rocky Mountains (=0.0025-0.0027; W =0.0021-0.0024), indicating substantial genetic drift in these isolated populations. Tajima's D was positive for all sites (D=0.240-0.811), consistent with recent contraction in population sizes range-wide. Conclusions: Substantial in uences of geography, elevation and climate variables on genetic differentiation were also detected and may interact with the regional effects of anthropogenic climate change to force the loss of unique genetic lineages through continued population extirpations in the Great Basin and Sierra Nevada.