Development of inventory datasets through remote sensing and direct observation data for earthquake loss estimation

This report summarizes the lessons learnt in extracting exposure information for the three study sites, Thessaloniki, Vienna and Messina that were addressed in SYNER-G. Fine scale information on exposed elements that for SYNER-G include buildings, civil engineering works and population, is one of the variables used to quantify risk. Collecting data and creating exposure inventories is a very time-demanding job and all possible data-gathering techniques should be used to address the data shortcoming problem. This report focuses on combining direct observation and remote sensing data for the development of exposure models for seismic risk assessment. In this report a summary of the methods for collecting, processing and archiving inventory datasets is provided in Chapter 2. Chapter 3 deals with the integration of different data sources for optimum inventory datasets, whilst Chapters 4, 5 and 6 provide some case studies where combinations between direct observation and remote sensing have been used. The cities of Vienna (Austria), Thessaloniki (Greece) and Messina (Italy) have been chosen to test the proposed approaches.JRC.G.5-European laboratory for structural assessmen

Feature Extraction from Remotely Sensed Imagery for Emergency Management and Environmental Assessment

L'abstract è presente nell'allegato / the abstract is in the attachmen

Automated Building Information Extraction and Evaluation from High-resolution Remotely Sensed Data

The two-dimensional (2D) footprints and three-dimensional (3D) structures of buildings are of great importance to city planning, natural disaster management, and virtual environmental simulation. As traditional manual methodologies for collecting 2D and 3D building information are often both time consuming and costly, automated methods are required for efficient large area mapping. It is challenging to extract building information from remotely sensed data, considering the complex nature of urban environments and their associated intricate building structures. Most 2D evaluation methods are focused on classification accuracy, while other dimensions of extraction accuracy are ignored. To assess 2D building extraction methods, a multi-criteria evaluation system has been designed. The proposed system consists of matched rate, shape similarity, and positional accuracy. Experimentation with four methods demonstrates that the proposed multi-criteria system is more comprehensive and effective, in comparison with traditional accuracy assessment metrics. Building height is critical for building 3D structure extraction. As data sources for height estimation, digital surface models (DSMs) that are derived from stereo images using existing software typically provide low accuracy results in terms of rooftop elevations. Therefore, a new image matching method is proposed by adding building footprint maps as constraints. Validation demonstrates that the proposed matching method can estimate building rooftop elevation with one third of the error encountered when using current commercial software. With an ideal input DSM, building height can be estimated by the elevation contrast inside and outside a building footprint. However, occlusions and shadows cause indistinct building edges in the DSMs generated from stereo images. Therefore, a “building-ground elevation difference model” (EDM) has been designed, which describes the trend of the elevation difference between a building and its neighbours, in order to find elevation values at bare ground. Experiments using this novel approach report that estimated building height with 1.5m residual, which out-performs conventional filtering methods. Finally, 3D buildings are digitally reconstructed and evaluated. Current 3D evaluation methods did not present the difference between 2D and 3D evaluation methods well; traditionally, wall accuracy is ignored. To address these problems, this thesis designs an evaluation system with three components: volume, surface, and point. As such, the resultant multi-criteria system provides an improved evaluation method for building reconstruction

Materials Democracy: An action plan for realising a redistributed materials economy

Human activities within the industrial economy are now the main and most significant drivers of change to the Earth System. These changes, driven by both the scale of human population and the magnifying effects of human technologies “are multiple, complex, interacting, often exponential in rate and globally significant in magnitude” (Steffen et al. 2004: 81). The years since the 1950s “have without doubt seen the most rapid transformation of the human relationship with the natural world in the history of humankind” (Steffen et al. 2004: 131). Over approximately the same period, the use of manufactured materials has increased by 4 to 15 times (Allwood et al. 2012: 7) and correlates with a rapid rise in global GDP. The expansion of the global economy is directly linked to the rise in land, sea and atmospheric pollution, natural habitat loss and the extraction and consumption of resources. Creating a future free of these destructive patterns will require the abandonment of the ‘take, make and throw away’ culture, moving toward a circular economy in which human wants and needs are met by managing resources at their highest utility for the longest time within biological and technical cycles. Without a wholesale recalibration of the global materials economy to factor in both immediate and long-term implications of all material decisions, inclusive of extraction to processing and transference to recapture, aspirations for a 21st-century circular economy will stall. This paper sets out a proposal for the development of an underlying common architecture and set of protocols for the generation, aggregation, and tracking of materials information in ways that are open, interoperable, and incorruptible. As such, materials information is envisioned as an open web of interconnected databases. The authors propose that such a materials information commons could empower stakeholders at all levels to make more effective decisions. For such an infrastructure to be operable and effective, a concerted and coordinated effort across all scales and sectors would need to be incentivised. This paper lays out the overarching context and need for such an undertaking and highlights both the opportunities and challenges therein. It surveys existing sources of materials information in order to expand upon and characterise recommended criteria for key material, technological, and behavioural functionalities. Lastly, this paper poses a number of areas of focus for future research

中国における都市化総合評価及び環境への影響に関する研究

In Chapter one, research background and significance is investigated. In addition, previous studies and current situation in the research fields was reviewed and discussed. In Chapter two, an in-depth review of prior studies associated with the research topic was conducted. The literature review was carried out from three aspects: urbanization and eco-environment evalution and coordination, urban sprawl assessment and urban heat island investigation. In Chapter three, maximum entropy method was applied to help generate the evaluation system of eco-environment level and urbanization level at provincial scale. Comparison analysis and coordinate analysis was carried through to assess the development of urbanization and eco-environment as well as the balance and health degree of the city develops. In Chapter four, DMSP/OLS stable nighttime light dataset was used to measure and assess the urban dynamics from the extraction of built up area. Urban sprawl was evaluated by analyzing the landscape metrics which provided general understanding of the urban sprawl and distribution pattern characteristics could be got from the evaluation. In Chapter five, the investigation of surface urban heat island effects in Beijing city which derive from land surface temperature retrieval from remote sensing data of Landsat TM was carried out. In addition, spatial correlation and relationship between the urbanization level, vegetation coverage and surface urban heat island was carried out in this chapter. In Chapter six, all the works have been summarized and a conclusion of whole thesis is deduced.北九州市立大

Hyperspectral LiDAR-Based Plant Spectral Profiles Acquisition : Performance Assessment and Results Analysis

In precision agriculture, efficient fertilization is one of the most important pursued goals. Vegetation spectral profiles and the corresponding spectral parameters are usually employed for vegetation growth status indication, i.e., vegetation classification, bio-chemical content mapping, and efficient fertilization guiding. In view of the fact that the spectrometer works by relying on ambient lighting condition, hyperspectral/multi-spectral LiDAR (HSL/MSL) was invented to collect the spectral profiles actively. However, most of the HSL/MSL works with the wavelength specially selected for specific applications. For precision agriculture applications, a more feasible HSL capable of collecting spectral profiles at wide-range spectral wavelength is necessary to extract various spectral parameters. Inspired by this, in this paper, we developed a hyperspectral LiDAR (HSL) with 10 nm spectral resolution covering 500~1000 nm. Different vegetation leaf samples were scanned by the HSL, and it was comprehensively assessed for wide-range wavelength spectral profiles acquirement, spectral parameters extraction, vegetation classification, and the laser incident angle effect. Specifically, three experiments were carried out: (1) spectral profiles results were compared with that from a SVC spectrometer (HR-1024, Spectra Vista Corporation); (2) the extracted spectral parameters from the HSL were assessed, and they were employed as the input features of a support vector machine (SVM) classifier with multiple labels to classify the vegetation; (3) in view of the influence of the laser incident angle on the HSL reflected laser intensities, we analyzed the laser incident angle effect on the spectral parameters values. The experimental results demonstrated the developed HSL was more feasible for acquiring spectral profiles with wide-range wavelength, and spectral parameters and vegetation classification results also indicated its great potentials in precision agriculture application

Cybergis-enabled remote sensing data analytics for deep learning of landscape patterns and dynamics

Mapping landscape patterns and dynamics is essential to various scientific domains and many practical applications. The availability of large-scale and high-resolution light detection and ranging (LiDAR) remote sensing data provides tremendous opportunities to unveil complex landscape patterns and better understand landscape dynamics from a 3D perspective. LiDAR data have been applied to diverse remote sensing applications where large-scale landscape mapping is among the most important topics. While researchers have used LiDAR for understanding landscape patterns and dynamics in many fields, to fully reap the benefits and potential of LiDAR is increasingly dependent on advanced cyberGIS and deep learning approaches. In this context, the central goal of this dissertation is to develop a suite of innovative cyberGIS-enabled deep-learning frameworks for combining LiDAR and optical remote sensing data to analyze landscape patterns and dynamics with four interrelated studies. The first study demonstrates a high-accuracy land-cover mapping method by integrating 3D information from LiDAR with multi-temporal remote sensing data using a 3D deep-learning model. The second study combines a point-based classification algorithm and an object-oriented change detection strategy for urban building change detection using deep learning. The third study develops a deep learning model for accurate hydrological streamline detection using LiDAR, which has paved a new way of harnessing LiDAR data to map landscape patterns and dynamics at unprecedented computational and spatiotemporal scales. The fourth study resolves computational challenges in handling remote sensing big data and deep learning of landscape feature extraction and classification through a cutting-edge cyberGIS approach