Integrating elevation data and multispectral high-resolution images for an improved hybrid Land Use/Land Cover mapping

ABSTRACTThe combination of elevation data together with multispectral high-resolution images is a new methodology for obtaining land use/land cover classification. It represents a step forward for both the accuracy and automation of LULC applications and allows users to setup thematic assignments through rules based on feature attributes and human expert interpretation of land usage. The synergy between different types of information means that LiDAR can give new hints at both the segmentation and hybrid classification steps, leading to a joint use of multispectral, spatial and elevation data. The output is a thematic map characterized by a custom-designed legend that is able to discriminate between land cover classes with similar spectral characteristics (level 3 of the CLC legend). Experimental results from a hilly farmland area with some urban structures (Musone river basin, Ancona, Italy) are used to highlight how the proposed methodology enhances land cover classification in heterogeneous environments

COMBINED MULTIPLE CLASSIFIED DATASETS CLASSIFICATION APPROACH FOR POINT CLOUD LIDAR DATA

Airborne Laser scanners using the Light Detection And Ranging (LiDAR) technology is a powerful tool for 3D data acquisition that records the backscattered energy as well. LiDAR has been successfully used in various applications including 3D modelling, feature extraction, and land cover information extraction. Airborne LiDAR data are usually acquired from different flight trajectories producing data in different strips with significant overlapped areas. Combining these data is required to get benefit of the multiple strips’ data that acquired from different trajectories. This paper introduces an approach called CMCD “Combined Multiple Classified Datasets” to maximize the benefits of the multiple LiDAR strips’ data in land cover information extraction. This approach relies on classifying each strip data then combining the results based on the a posteriori probability of each class of the classified data and the position of the classified points.Two datasets from different overlapped areas are selected to test the proposed CMCD approach; both are captured from different flight trajectories. A comparison has been conducted between the CMCD results and the results of the common merging data approaches. The results indicated that the classification accuracy of the proposed CMCD approach has improved the classification accuracy of the merged data-layers by 6% and 10% for the two datasets.</p

Deep learning for forest inventory and planning : a critical review on the remote sensing approaches so far and prospects for further applications

Data processing for forestry applications is challenged by the increasing availability of multi-source and multi-temporal data. The advancements of Deep Learning (DL) algorithms have made it a prominent family of methods for machine learning and artificial intelligence. This review determines the current state-of-the-art in using DL for solving forestry problems. Although DL has shown potential for various estimation tasks, the applications of DL to forestry are in their infancy. The main study line has related to comparing various Convolutional Neural Network (CNN) architectures between each other and against more shallow machine learning techniques. The main asset of DL is the possibility to internally learn multi-scale features without an explicit feature extraction step, which many people typically perceive as a black box approach. According to a comprehensive literature review, we identified challenges related to (1) acquiring sufficient amounts of representative and labelled training data, (2) difficulties to select suitable DL architecture and hyperparameterization among many methodological choices and (3) susceptibility to overlearn the training data and consequent risks related to the generalizability of the predictions, which can however be reduced by proper choices on the above. We recognized possibilities in building time-series prediction strategies upon Recurrent Neural Network architectures and, more generally, re-thinking forestry applications in terms of components inherent to DL. Nevertheless, DL applications remain data-driven, in contrast to being based on causal reasoning, and currently lack many best practices of conventional forestry modelling approaches. The benefits of DL depend on the application, and the practitioners are advised to ex ante subject their requirements to operational data availability, for example. By this review, we contribute to the technical discussion about the prospects of DL for forestry and shed light on properties that require attention from the practitioners.Peer reviewe

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

From roofs to renewables : Deep learning and geographic information systems insights into a comprehensive urban solar photovoltaic assessment for Stonehaven

Peer reviewe

Patient innovation : its prevalence, antecedents and impact

In order to maintain the level of healthcare that we now associate with developed countries, innovation is imperative. Previous literature has shown that patients of chronic diseases are often involved in the development of new treatments and medical devices to help them cope with their health-condition. However the innovation developed by patients is often ignored or even rejected. A possible approach to address this issue is to open up healthcare innovation, by allowing patients and their caregivers to become themselves active contributors to the innovation process. In this context, the aim of this dissertation is to (1) quantify the extent to which patients and caregivers of rare diseases innovate, (2) find demographic and non-demographic antecedents of patient innovation, and (3) assess how the innovations impact the lives of patients. A telephone survey was conducted with the main objective of measuring the extent to which respondents had innovated, or not. 496 patients and caregivers of 250 rare diseases responded to the survey. Following the data collection we performed a descriptive analysis of the data and a multiple logistic regression to identify statistically relevant predictor variables of patient innovators. We found that 13% of respondents had innovated, and the variables that emerged as predictors of being an innovator are: higher level of education, being unemployed or looking after at home, being aware of the expenses with the disease, and Information and Communication Technology readiness. On the other side, being single has a negative impact on the propensity to innovate when compared with being married. Moreover, in a 7-point Likert scale measuring the quality of life of the patient, the innovations led to an average improvement of 2.4 points. Not only are patients developing completely new-to-the-market innovations, that are improving the patients’ quality of life, but they also assume the risks of trying solutions that had not yet been tested. This study suggests that the current producer-based and paternalistic healthcare model should be revised, so patients are given the chance of playing a more proactive role.De forma a mantermos a saúde nos níveis que hoje associamos com países desenvolvidos, a inovação é imperativa. Estudos anteriores revelaram que em vários casos pacientes de doenças crónicas estão envolvidos no desenvolvimento de novos tratamentos e equipamentos médicos que os ajudam a lidar com a sua condição. Contudo, as inovações desenvolvidas por pacientes são frequentemente ignoradas ou até rejeitadas. Uma possível solução para superar esta crise de inovação seria deixar que pacientes e cuidadores se tornassem eles próprios contribuidores ativos no processo de inovação. Deste modo o objectivo desta dissertação é (1) quantificar até que ponto pacientes e cuidadores de doenças raras inovam, (2) identificar antecedentes demográficos e não-demográficos de inovação por pacientes, e (3) avaliar o modo como as inovações por pacientes afetam a vida dos pacientes. Conduziu-se um questionário telefónico com o fim de determinar até que ponto os entrevistados teriam inovado ou não. 496 pacientes e cuidadores de 250 doenças raras responderam ao questionário. Após a recolha de dados, efetuámos uma análise descritiva dos dados bem como uma regressão logística múltipla de forma a identificar variáveis estatisticamente relevantes preditoras do fenómeno de inovação por pacientes. Constatámos que 13% dos respondentes inovaram. As variáveis que emergiram como preditoras foram: educação superior, estar desempregado ou ser doméstico, estar ciente das despesas com a doença, utilização de tecnologias de informação. Por outro lado, ser solteiro, quando comparado com ser casado, tem um impacto negativo na propensão para inovar. Adicionalmente, numa escala de Likert de sete pontos que mediu a qualidade de vida do paciente, denotou-se uma melhoria média de 2.4 pontos após a inovação. Estes indivíduos não apenas desenvolvem inovações que melhoram a qualidade de vida dos pacientes, mas assumem também o risco de experimentar soluções que não foram ainda testadas. Este estudo sugere que o atual modelo de saúde paternalista, cujo epicentro são os produtores, deverá ser revisto tendo em vista a possibilidade de pacientes assumirem um papel mais proactivo

Investigation of Coastal Vegetation Dynamics and Persistence in Response to Hydrologic and Climatic Events Using Remote Sensing

Coastal Wetlands (CW) provide numerous imperative functions and provide an economic base for human societies. Therefore, it is imperative to track and quantify both short and long-term changes in these systems. In this dissertation, CW dynamics related to hydro-meteorological signals were investigated using a series of LANDSAT-derived normalized difference vegetation index (NDVI) data and hydro-meteorological time-series data in Apalachicola Bay, Florida, from 1984 to 2015. NDVI in forested wetlands exhibited more persistence compared to that for scrub and emergent wetlands. NDVI fluctuations generally lagged temperature by approximately three months, and water level by approximately two months. This analysis provided insight into long-term CW dynamics in the Northern Gulf of Mexico. Long-term studies like this are dependent on optical remote sensing data such as Landsat which is frequently partially obscured due to clouds and this can that makes the time-series sparse and unusable during meteorologically active seasons. Therefore, a multi-sensor, virtual constellation method is proposed and demonstrated to recover the information lost due to cloud cover. This method, named Tri-Sensor Fusion (TSF), produces a simulated constellation for NDVI by integrating data from three compatible satellite sensors. The visible and near-infrared (VNIR) bands of Landsat-8 (L8), Sentinel-2, and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were utilized to map NDVI and to compensate each satellite sensor\u27s shortcomings in visible coverage area. The quantitative comparison results showed a Root Mean Squared Error (RMSE) and Coefficient of Determination (R2) of 0.0020 sr-1 and 0.88, respectively between true observed and fused L8 NDVI. Statistical test results and qualitative performance evaluation suggest that TSF was able to synthesize the missing pixels accurately in terms of the absolute magnitude of NDVI. The fusion improved the spatial coverage of CWs reasonably well and ultimately increases the continuity of NDVI data for long term studies

Review of Wind Flow Modelling in Urban Environments to Support the Development of Urban Air Mobility

Urban air mobility (UAM) is a transformative mode of air transportation system technology that is targeted to carry passengers and goods in and around urban areas using electric vertical take-off and landing (eVTOL) aircraft. UAM operations are intended to be conducted in low altitudes where microscale turbulent wind flow conditions are prevalent. This introduces flight testing, certification, and operational complexities. To tackle these issues, the UAM industry, aviation authorities, and research communities across the world have provided prescriptive ways, such as the implementation of dynamic weather corridors for safe operation, classification of atmospheric disturbance levels for certification, etc., within the proposed concepts of operation (ConOps), certification standards, and guidelines. However, a notable hindrance to the efficacy of these solutions lies in the scarcity of operational UAM and observational wind data in urban environments. One way to address this deficiency in data is via microscale wind modelling, which has been long established in the context of studying atmospheric dynamics, weather forecasting, turbine blade load estimation, etc. Thus, this paper aims to provide a critical literature review of a variety of wind flow estimation and forecasting techniques that can be and have been utilized by the UAM community. Furthermore, a compare-and-contrast study of the commonly used wind flow models employed within the wind engineering and atmospheric science domain is furnished along with an overview of the urban wind flow conditions