The population growth in many urban cities and its activities in developing countries have resulted in an increased solid waste generation rate and waste management has become a global environmental issue. Routing of solid waste collection vehicles in developing countries like Pakistan poses a challenging task. In the process of solid waste management, collection and transportation play a leading role in waste collection and disposal, in which collection activities contributed the most to total cost for solid waste collection activities. Therefore, this study describes an attempt to design and develop an appropriate collection, transportation and disposal plan for the twin cities of Pakistan by using Geographic Information System (GIS) and Remote Sensing (RS) techniques to determine the minimum cost/distance/time efficient collection paths for the transportation of the solid wastes to the landfill sites. In addition to this, identification of solid waste disposal sites and appropriately managing them is a challenging task to many developing countries and Pakistan is no exception to that. The existing landfill sites for the twin cities are not technically viable and environmentally acceptable and are thus damaging to the environment due to their location and the type of waste dumped. Therefore, the second aim of our study was to find out the suitable landfill sites for the twin cities and the study employed Multi-Criteria Evaluation (MCE) methods to combine necessary factors considered for landfill site selection for the twin cities. Hence, our present study has proved that GIS is a tool that can be used in integration with other techniques such as MCE for a identifying new landfill sites and it can help decision makers deal with real-world developmental and management issues. Finally, the study has developed a Wed-Based Decision Support System (DSS) via Application Programming Interface (API) which will help decision-makers to search for cost-effective alternatives and it can be operated by people who don’t have knowledge of GIS. The proposed study can be used as a decision support tool by the municipalities of the twin cities for efficient management and transportation of solid wastes to landfill sites, managing work schedules for workers, etc.

In this thesis a clustering of the Stockholm county housing market has been performed using different clustering methods. Data has been derived and different geographical constraints have been used. DeSO areas (Demographic statistical areas), developed by SCB, have been used to divide the housing market in to smaller regions for which the derived variables have been calculated. Hierarchical clustering methods, SKATER and Gaussian mixture models have been applied. Methods using different kinds of geographical constraints have also been applied in an attempt to create more geographically contiguous clusters. The different methods are then compared with respect to performance and stability. The best performing method is the Gaussian mixture model EII, also known as the K-means algorithm. The most stable method when applied to bootstrapped samples is the ClustGeo-method. / I denna uppsats har en klustring av Stockholms läns bostadsmarknad genomförts med olika klustringsmetoder. Data har bearbetats och olika geografiska begränsningar har använts. DeSO (Demografiska Statistiska Områden), som utvecklats av SCB, har använts för att dela in bostadsmarknaden i mindre regioner för vilka områdesattribut har beräknats. Hierarkiska klustringsmetoder, SKATER och Gaussian mixture models har tillämpats. Metoder som använder olika typer av geografiska begränsningar har också tillämpats i ett försök att skapa mer geografiskt sammanhängande kluster. De olika metoderna jämförs sedan med avseende på kvalitet och stabilitet. Den bästa metoden, med avseende på kvalitet, är en Gaussian mixture model kallad EII, även känd som K-means. Den mest stabila metoden är ClustGeo-metoden.

<p>Accurate 3D landscape models of cities or mountains have wide applications in mission</p> <p>planning, navigation, geological studies, etc. Lidar scanning using drones can provide high</p> <p>accuracy 3D landscape models, but the data is more expensive to collect as the area of</p> <p>each scan is limited. Thanks to recent maturation of Very-High-Resolution (VHR) optical</p> <p>imaging on satellites, people nowadays have access to stereo images that are collected on a</p> <p>much larger area than Lidar scanning. My research addresses unique challenges in satellite</p> <p>stereo, including stereo rectification with pushbroom sensors, dense stereo matching using</p> <p>image pairs with varied appearance, e.g. sun angles and surface plantation, and rasterized</p> <p>digital surface model (DSM) generation. The key contributions include the Continuous 3D-</p> <p>Label Semi-Global Matching (CoSGM) and a large scale dataset for satellite stereo processing</p> <p>and DSM evaluation.</p>

This work includes modelling of decarbonization scenarios at the scale of an urban area, providing policy insights and a methodology focusing on introducing district heating and maintaining the existing gas distribution network in a case study. With a focus on reducing gas consumption in the residential and tertiary sectors, the research integrates scenario developments giving a methodology to develop district heating, requiring a careful balance in selecting the optimal scale for city-wide analysis. The study assesses the fate of existing gas networks. The development of district heating can affect the use of gas, particularly in residential or tertiary buildings. This thesis assesses potential use cases of existing gas networks by identifying certain criteria. Among them are industrial, tertiary or residential consumption, the presence of a district heating network, or the number of homes using individual gas heating. These criteria make it possible to define areas where the question of removing the gas distribution network can be raised, and other areas where the gas distribution network must be retained even if gas consumption falls sharply between 2019 and 2050. By reviewing the relevant literature, detailing the research questions and presenting a comprehensive methodology of scenario modelling, the thesis provides policy insights and a methodology to develop district heating at the scale of an urban area while addressing the future of existing gas infrastructure. / Detta arbete ger en modellering av scenarier för minskade koldioxidutsläpp i stadsområden, samt ger policyinsikter och metodik med fokus på införandet av fjärrvärme och underhållet av det befintliga gasdistributionsnätet som en fallstudie. Med fokus på att minska gasförbrukningen i bostads- och tjänstesektorerna integrerar forskningen scenarioutveckling med en metod för att utveckla fjärrvärme, vilket kräver en noggrann avvägning för att välja den optimala skalan för stadsomfattande analys. I studien bedöms vad som ska hända med befintliga gasnät. Utvecklingen av fjärrvärme kan påverka användningenav gas, särskilt i bostads- eller tertiärbyggnader. Denna avhandling bedömer potentiella användningsfall befintliga gasnät genom att identifiera kriterier baserade på faktorer som industriell, tertiär tertiär eller bostadsförbrukning, förekomsten av ett fjärrvärmenät eller antalet av bostäder som använder individuell gasuppvärmning, till exempel. Dessa kriterier gör det möjligt att definiera områden där frågan om att ta bort gasdistributionsnätet kan väckas, och andra områden där gasdistributionsnätet måste behållas även om förbrukningen förbrukningen minskar kraftigt mellan 2019 och 2050. Genom att granska den relevanta litteraturen, specificera forskningsfrågorna och presentera en omfattande metod för scenariomodellering, ger avhandlingen ett värdefullt exempel på hur man kan ge politisk insikt och metodik för att utveckla fjärrvärme i ett stadsområde samtidigt som man tar itu med framtiden för befintlig gasinfrastruktur.

<p dir="ltr">Canopy height is a fundamental metric for extracting valuable information about forested areas. Over the past decade, Lidar technology has provided a straightforward approach to measuring canopy height using various platforms such as terrestrial, unmanned aerial vehicle (UAV), airborne, and satellite sensors. However, satellite Lidar data, even with its global coverage, has a sparse sampling pattern that doesn’t provide continuous coverage over the globe. In contrast, satellites like LANDSAT offer seamless and widespread coverage of the Earth's surface through spectral data. Can we exploit the abundant spectral information from satellites like LANDSAT and ECOSTRESS to infer structural information obtained from Lidar satellites like Global Ecosystem Dynamic Investigation (GEDI)? This study aims to develop a deep learning model that can infer canopy height derived from sparsely observed Lidar waveforms using multi-sensor spectral data from spaceborne platforms. Specifically designed for localized site, the model focuses on county-level canopy height estimation, taking advantage of the relationship between canopy height and spectral reflectance that can be established in a local setting – something which might not exist universally. The study hopes to achieve a framework that can be easily replicable as height is a dynamic metric which changes with time and thus requires repeated computation for different time periods.</p><p dir="ltr">The thesis presents a series of experiments designed to comprehensively understand the influence of different spectral datasets on the model’s performance and its effectiveness in different types of test sites. Experiment 1 and 2 utilize Landsat spectral band values to extrapolate canopy height, while Experiment 3 and 4 incorporate ECOSTRESS land surface temperature and emissivity band values in addition to Landsat data. Tippecanoe County, predominantly composed of cropland, serves as the test site for Experiment 1 and 3, while Monroe County, primarily covered by forests, serves as the test site for Experiment 2 and 4. When compared to the Airborne Lidar dataset from the United States Geological Survey (USGS) – 3D Elevation Program (3DEP), the model achieves a Root Mean Square Error (RMSE) of 4.604m for Tippecanoe County using Landsat features while 5.479m for Monroe County. After integrating Landsat and ECOSTRESS features, the RMSE improves to 4.582m for Tippecanoe County but deteriorates to 5.860m for Monroe County. Overall, the study demonstrates comparable results to previous research without requiring feature engineering or extensive pre-processing. Furthermore, it successfully introduces a novel methodology for integrating multiple sources of satellite data to address this problem.</p>

Nationell infrastruktur för geografiska data (NSDI) har blivit en viktig pusselbit för varje nation när det gäller socio-ekonomisk utveckling och miljöförvaltning. Den nyligen antagna lagen The Lands Rights Act (2018) och inrättandet av Liberia Land Authority (LLA) visar att Liberia står på randen till en seriös utveckling och visar vägen genom att inrätta ett välfungerande system för markförvaltning och en pålitlig markförvaltning. Infrastruktur för geografiska data (SDI) är viktigt för att hantera och möjliggöra utbyte, delning, tillgänglighet och användning av rumsliga data. FN:s expertkommitté för global hantering av geospatial information (UN-GGIM) och Världsbanken har utvecklat Integrated Geospatial Information Framework (IGIF) för att främja hållbar utveckling och tillhandahålla riktlinjer för nationer att följa när de utvecklar en robust NSDI. Syftet med denna studie är att bedöma Liberia´s NSDI från ett data och standardperspektiv för att identifiera landets svagheter och styrkor inom detta område. Den kommer också att belysa de utmaningar och möjligheter som Liberia står inför när landet utvecklar sitt NSDI. Data samlades in genom en litteraturgenomgång och frågeformulär som fylldes i med NSDI-intressenter vid flera statliga organisationer och en internationell organisation i Liberia. Resultaten visade att Liberia´s NSDI för närvarande är underutvecklat. NSDI anses vara svagt ur ett data och standardperspektiv. Dataperspektivet anses dock vara mer gediget. En generell lägesbedömning som täcker alla nio aspekter av ett NSDI var också genomförd, detta i syfte att sätta de två specifika perspektiven i kontext. Bristen på nationella standarder, institutionell samordning och en rättslig ram för hantering av geografiska data är de främsta problemen och utmaningarna. LLA bör ta ledning i utvecklingen av Liberia´s NSDI. I studien föreslås också att Liberia bland annat ska bilda en kommitté för geografiska data för att få alla relevanta intressenter involverade och engagerade i den kommande NSDI-utvecklingen. / National Spatial Data Infrastructure (NSDI) has become an important piece of the puzzle for every nation when it comes to socio-economic development and environmental stewardship. The recently passed The Land Rights Act (2018) and the establishment of the Liberia Land Authority (LLA) indicates that Liberia is on the verge of serious development, paving the way by establishing a well-functioning land administration system and trusted land governance. Spatial data infrastructure is important in order to manage and enable the exchange, sharing, accessibility and use of spatial data. United Nations Committee of Experts on Global Geospatial Information Management (UN-GGIM) and the World Bank has developed the Integrated Geospatial Information Framework (IGIF) to promote sustainable development and provide guidelines for nations to follow when developing a robust NSDI. The objectives of this paper is to assess Liberia's NSDI from a data and standards perspective to identify its weaknesses and strengths within that area. It will also shed light on the challenges and opportunities that Liberia faces as it develops its NSDI. The data was collected through a literature review and questionnaires were completed by NSDI stakeholders from multiple governmental organizations and one international organization in Liberia. The findings revealed that Liberia's NSDI is currently underdeveloped. The NSDI is considered weak from a data and standards perspective. However, the data perspective is considered as more solid. A general baseline assessment covering all aspects of an NSDI was also carried out to set the two specific perspectives into context. Overall, weak national standards, institutional coordination, and legal framework for handling spatial data are the primary concerns and challenges. LLA is suggested to take the lead in the development of Liberia´s NSDI. The study also suggests that Liberia form a spatial data committee in order to have all relevant stakeholders onboard and committed for the NSDI development at hand.

Right-of-way business processes have been identified as one of the areas where information bottlenecks occur in transportation agencies, not only because of the amount of information involved, but also because of the interdependent nature of these activities. Transportation projects are associated with parcels whose status change during and after the project based on information from right-of-way activities. Geospatially enabled decision making tools enhance data interpretation as well as data retrieval of this information. By using enterprise-level applications, information sharing between the transportation agency, other jurisdictions, and the public becomes more efficient. Being able to quickly visualize the status of parcels in a geospatial environment further enhances the management of resources which, in turn, improves timely project delivery. This thesis presents a conceptual framework for an information management system and its geospatial enablement through a logical model for Geospatial Decision Making Activities (GDMA) in transportation right-of-way offices. The logical model for GDMA, presented in Unified Modeling Language, includes state machine diagram and data flow diagram models for tracking the parcel and capturing the geospatial enablement of right-of-way activities. / Master of Science

<p dir="ltr">We explore the efficacy of sparsity and ensemble model in the classification of hyperspectral images, a pivotal task in remote sensing applications. While Convolutional Neural Networks (CNNs) and Transformer models have shown promise in this domain, each exhibits distinct limitations; CNNs excel in capturing the spatial/local features but falter to capture spectral features, whereas Transformers captures the spectral features at the expense of spatial features. Furthermore, the computational cost associated with training several independent CNN and Transformer networks becomes expensive. To address these limitations, we propose a novel ensemble framework comprising pruned CNNs and Transformers, optimizing both spatial and spectral feature utilization while curbing computational costs. By integrating sparsity through model pruning, our approach effectively reduces redundancy and computational complexity without compromising accuracy. Through extensive experimentation, we find that our method achieves comparable accuracy to its non-sparse counterparts while decreasing the computational cost. Our contribution enhances remote sensing analytics by demonstrating the potential of sparse and ensemble models in improving the precision and computational efficiency of hyperspectral image classification.</p>

<p>Integrating unoccupied aerial systems (UAS) into agricultural remote sensing has revolutionized several domains, including crop yield estimation. This research arises from the need to combat citrus greening disease, a major threat to citrus production. Accurately estimating crop yields is crucial for evaluating the effectiveness of treatments and controls for this disease. In response, our study examined the efficacy of phenotypic data extracted from multi-temporal RGB and multispectral UAS images in estimating individual citrus tree yields before harvest and then using this as an indicator to analyze the effectiveness of the treatments and control choice.</p> <p>This study presents machine learning-based regression models for estimating individual citrus tree yields, utilizing the diverse features extracted to provide comprehensive insights into the citrus trees under investigation. Four machine learning algorithms, random forest regression, extreme gradient boosting regression, adaptive boosting, and support vector regression, were employed to build the yield estimation models. The experiment was designed in two phases: single-temporal and multi-temporal modeling.</p>

Rivers are inherently dynamic environments with fluctuations in water quality, hydrology, connectivity and geomorphology. Though geomorphology has long been recognized as an important driver defining biological, ecological, and physical habitat characteristics of rivers, a readily applied classification tool that links such characteristics has been lacking. The Geomorphic Response Unit (GRU) method provides a novel approach to identifying large scale patterns in geomorphic character that provide a link between the hydrological regime and different habitat types to which species respond. Specifically, I investigated whether Geomorphic Types and GRUs are related to the distribution and abundance of different fish species, reflecting unique physical habitat characteristics of individual GRUs. The thesis chapters are manuscript based. The second chapter identifies relationships between specific Geomorphic Types, identified using the Geomorphic Response Unit (GRU) methodology, and Lake Sturgeon overwintering locations in the South Saskatchewan and Saskatchewan Rivers. Habitat selection ratios suggest that Lake Sturgeon in the Upper South Saskatchewan River significantly selected for one of seven possible Types for overwintering. Logistic regression results found both Type 0 and Type 4 predicted significantly higher Sturgeon presence than all other Types (P = < 2e-16 for both). The third chapter examines relationships between GRUs and abundance of both mature and immature Carmine Shiner in the Birch River, Manitoba. Differences in the median mature Carmine Shiner CPUEs among the GRUs are not statistically different (Kruskal-Wallis test H =1.723; df = 3, p value = 0.632), though interesting qualitative relationships were identified which may inform further studies. The fourth chapter investigates whether GRUs derived using a large scale network approach are linked to the abundance of specific fish species in the Assiniboine River, Manitoba. A Kruskal-Wallis test identified significant differences in CPUE among GRUs for 10 of 14 tested species. Post-hoc pairwise multiple comparisons using Dunn’s Method with Bonferroni p-value correction for multiple paired tests isolated the GRUs that were different from one another. Overall, my findings suggest that Geomorphic Response Units (GRU) are an effective means of identifying patterns in geomorphic structure within Prairie Rivers at both reach and segment scales. Further, I identified links between both Geomorphic Types and GRUs and patterns in abundance of various fish species covering a wide range of life history traits. These findings suggest that GRUs have potential as a valuable fisheries habitat management tool, increasing efficiency of monitoring efforts through quantification of habitat availability, connectivity, and complexity in Prairie River systems.