구글 스트릿뷰를 이용한 도시 협곡 내 평균복사온도 추정

학위논문 (석사) -- 서울대학교 대학원 : 농업생명과학대학 생태조경학과, 2021. 2. 이동근.도시개발로 인해 보행자의 에너지 균형을 변화시키며 도시공간의 열 쾌적성이 악화되는 등 열 환경문제가 발생하고 있다. 선행연구에서는 도시 공간 내 열 쾌적성을 정량적으로 평가하기 위해 인간의 가장 중요한 생체 기상 변수 중 하나인 평균복사온도를 산정하는 연구가 진행되고 있다. 하지만 산정식이 복잡하거나, 넓은 범위에서의 공간 데이터 취득이 어렵기 때문에, 커뮤니티 단위에서 고해상도의 평균복사온도를 추정하는 것은 어렵다. 따라서 본 연구에서는 구글스트릿뷰 이미지를 사용하여 도시 거리 협곡내 평균복사 온도를 추정하는 방법을 제시하고, 도시 스케일에서 도시열섬 분석을 위해 많은 연구가 진행된 지표면 온도와 평균복사온도간 관계를 공간패턴 측면에서 분석하였다. 우선 평균복사온도 추정식에 큰 영향을 미치는 천공률은 파노라마 이미지를 바탕으로 딥러닝을 활용하여 도시 요인별(건물, 나무, 하늘 등)분류하고, 어안렌즈 이미지로 변환하여 도출하였다. 또한 어안렌즈 이미지를 중심으로 태양경로 알고리즘을 활용하여 시간별 그림자의 유무를 판단하였다. 마지막으로 기후요인, 시간, 위치 등 데이터를 활용하여 장파, 단파 복사를 도출하여 평균복사온도를 산정하였다. 제안된 평균복사온도 추정 방법과 실측간 비교(7 곳) 결과 단파, 장파 값의 R^2값이 각각 0.97, 0.77로 나타났다. 다른 모델과 비교한 결과, 높은 정확도를 확인할 수 있으며 복잡한 도시 환경에서의 활용가능성을 확인할 수 있다. 도시규모에서 지표면온도, 평균복사온도를 공간패턴 측면에서 비교한 결과 천공률, 빌딩 뷰팩터가 각각 0.6~1.0, 0.35-0.5인 오픈스페이스 혹은 저층 밀집지역에서 높은 평균복사온도(>59.4°C)를 보였다. 반면 높은 빌딩이 밀집된 지역의 경우(빌딩 뷰팩터 :0.4-0.6, 나무 뷰팩터 0.6-0.9) 낮은 평균복사온도(<47.6°C)를 보였다. 특히 거리의 방향이 동-서 인 경우에는 천공률이 0.3-0.55 일지라도 높은 평균복사온도를 확인할 수 있었다. 추가적으로 평균복사온도와 지표면 온도간 비교결과 전반적으로 높은 온도 값을 가진 공간이 유사하였으나, 저층 고밀도 건물 지역 혹은 초지 지역에서 상반된 결과를 확인할 수 있었다. 본 연구에서는 도시스케일에서 높은 해상도로 평균복사온도를 추정하는 방법을 딥러닝을 활용하여 제시하였으며, 지표면 온도와 공간패턴별 분석을 통해 실제 보행자가 체감하는 열 환경을 개선하기 위한 방안을 제시할 수 있는 기초자료를 제공하였다. 이는 도시 열 환경을 고려한 지속가능한 도시 공간 설계 및 환경 계획 측면에서 활용 될 수 있으며, 특히 공간데이터 취득이 어려운 곳에서의 높은 활용성을 기대해 볼 수 있다.This paper presents a method for estimating Mean Radiant Temperature (MRT) of street canyons using Google Street View (GSV) images and investigates its spatial patterns in street-level on large scale. We used image segmentation using deep learning, project panorama to fisheye image and sun path algorithms to estimate MRT using GSV. Verification of proposed method can be explained by total of 7 field measurements in clear-sky of street-level, since the estimated shortwave and longwave radiation of value is 0.97, 0.77 respectively. The method proposed in this study is suitable for actual complex urban environment consisting of buildings, tree and streets. Additionally, we compared calculated MRT and LST (Land Surface Temperature) from Landsat 8 in a city scale. As a result of investigating spatial patterns of MRT in Seoul, We found that Higher MRT of street canyons ( >59.4℃) is mainly distributed in open space areas and compact low-rise density building where SVF (Sky View Factor) is 0.6–1.0 and BVF(Building View Factor) is 0.35–0.5, or West-East orientation street canyons with SVF(0.3–0.55). On the other hand, high density building (BVF is 0.4–0.6) or high density tree areas (TVF (Tree View Factor) is 0.6–0.99) showed Low MRT ( < 47.6). The mapped MRT results had similar spatial distribution with LST, but the MRT(?) lower (?) than LST in low tree density or low-rise high-density building areas. And it will help decision makers how to improve thermal comfort at the street-level.Chapter 1. Introduction １ 1.1. Study Background １ 1.2. Literature review ４ 1.2.1 Mean radiant temperature formula ４ 1.2.2 Surface temperature simulation model ５ Chapter 2. Study area and data １０ 2.1. Study area １０ 2.2. Data collection １１ Chapter 3. Method １３ 3.1. Research flow １３ 3.2. MRT simulation １４ 3.2.1. Schematic flow for MRT simulation １４ 3.2.2. Urban canyon geometry calculation using GSV images (Phase I: built geometry data) １６ 3.2.3. Street canyon solar radiation calculation (Phase II:radiation transfer calculation.) １７ 3.2.3.1 Calculation of street-level shortwave radiation １７ 3.2.3.2 Calculation of street-level long-wave radiation １９ 3.2.4. Phase III mean radiation temperature calculation ２１ Chapter 4. Result and Discussion ２２ 4.1. verification of solar radiation estimated in street-level ２２ 4.2. Validation of Long-wave radiation ２４ 4.3. Comparison between LST and MRT estimated using GSV ２６ 4.4. Comparison of GSV_MRT with other models ２９ 4.5. limitations and future development ３２ Chapter 5. Conclusion ３４ Bibliography ３６ Abstract in Korean ４３ Appendix ４５Maste

Fingerprint location methods using ray-tracing

Mobile location methods that employ signal fingerprints are becoming increasingly popular in a number of wireless positioning solutions. A fingerprint is a spatial database, created either by recorded measurement or simulation, of the radio environment. It is used to assign signal characteristics such as received signal strength or power delay profiles to an actual location. Measurements made by either the handset or the network, are then matched to those in the fingerprint in order to determine a location. Creation of the fingerprint by an a priori measurement stage is costly and time consuming. Virtual fingerprints, those created by a ray-tracing radio propagation prediction tool, normally require a lengthy off-line simulation mode that needs to be repeated each time changes are made to the network or built environment. An open research question exists of whether a virtual fingerprint could be created dynamically via a ray-trace model embedded on a mobile handset for positioning purposes. The key aim of this thesis is to investigate the trade-off between complexity of the physics required for ray-tracing models and the accuracy of the virtual fingerprints they produce. The most demanding computational phase of a ray-trace simulation is the ray-path finding stage, whereby a distribution of rays cast from a source point, interacting with walls and edges by reflection and diffraction phenomena are traced to a set of receive points. Due to this, we specifically develop a new technique that decreases the computation of the ray-path finding stage. The new technique utilises a modified method of images rather than brute-force ray casting. It leads to the creation of virtual fingerprints requiring significantly less computation effort relative to ray casting techniques, with only small decreases in accuracy. Our new technique for virtual fingerprint creation was then applied to the development of a signal strength fingerprint for a 3G UMTS network covering the Sydney central business district. Our main goal was to determine whether on current mobile handsets, a sub-50m location accuracy could be achieved within a few seconds timescale using our system. The results show that this was in fact achievable. We also show how virtual fingerprinting can lead to more accurate solutions. Based on these results we claim user embedded fingerprinting is now a viable alternative to a priori measurement schemes

A new simplified vector-based model to support solar energy planning at urban scale

Evaluation of solar potential is a necessary step for integrating solar technologies in buildings in order to properly assess the benefits of harvesting solar energy and draw well-informed decisions in various design phases. Solar energy planning at urban scale requires large-scale solar analysis to support various decision-making contexts, such as making urban solar targets, prioritizing urban zones or buildings for solar integration, and optimizing solar technologies tailored for targeting buildings. Existing tools have the following major limitations to support such decision-making situations. (1) Current advanced simulation models based on ray trace and ray interception techniques are not effectively scalable to evaluate solar potential at urban scale due to the expensive modelling process and computational cost. (2) Simple and statistical models developed for large-scale analysis are not suitable to accurately predict solar irradiance on individual surfaces with proper consideration of urban shading and reflection. This dissertation addresses the need for developing scalable, efficient analysis methods to support the solar energy planning process. This dissertation has developed a simplified vector-based model that effectively predicts the solar potential of urban areas on the basis of consideration of the urban context. The proposed model is based on vector-based methods without the use of ray trace and ray interception techniques, and consists of new methods that suitably account for the non-uniform solar radiation of the sky, obstruction by urban surfaces, and reflection by urban surfaces in urban areas. The proposed model establishes three new methods to simplify the calculation in the context of urban applications: (1) a two-segment discretisation model, (2) an edge-angle detection obstruction model, and (3) a unified view-angle-based reflection model. This dissertation demonstrates the usability of the new model in supporting decision-making in the solar energy planning process. It addresses the following two hypotheses to examine the usability of the new model: (1) Simplified, vector-based model, tailored to urban applications, predict accurate solar radiation on urban surfaces to effectively support urban-scale analysis and (2) solar analysis with full representation of urban surroundings is necessary in the calculation of urban shading and solar reflection to correctly support distributed PV planning. For the first hypothesis, the performance of the method is compared against the advanced daylight simulation program RADIANCE and measurements obtained from controlled experiments. The first comparison demonstrates the new method provides flexible setting options for different resolution and prediction accuracy requirements and generates reasonably accurate predictions. The second comparison further confirms the prediction accuracy against the measurements for the horizontal and vertical surfaces under different shading and reflection conditions. The comparison with the ray interception approach demonstrates the computational efficiency of the proposed obstruction model for solar analysis that substantially reduces calculation iterations for detecting sky and building obstructions. For the second hypothesis, predictions and decisions derived by the developed method are compared against those by a lower fidelity models to investigate the importance of modelling urban shading and reflection with full representation of urban surroundings in three decision making contexts of urban-scale distributed PV planning process. Additionally, the second hypothesis is furthered examined and highlighted by investigating the effect of an additional dynamic PV model on decision-makings in comparison with the effect of the proposed high-fidelity solar radiation model for urban shading and solar reflection. The new model is demonstrated to enable cost-efficient solar potential analysis based on urban contexts for supporting solar energy planning at urban scale.CSC Cambridge Scholarshi

Intelligent GNSS Positioning using 3D Mapping and Context Detection for Better Accuracy in Dense Urban Environments

Conventional GNSS positioning in dense urban areas can exhibit errors of tens of meters due to blockage and reflection of signals by the surrounding buildings. Here, we present a full implementation of the intelligent urban positioning (IUP) 3D-mapping-aided (3DMA) GNSS concept. This combines conventional ranging-based GNSS positioning enhanced by 3D mapping with the GNSS shadow-matching technique. Shadow matching determines position by comparing the measured signal availability with that predicted over a grid of candidate positions using 3D mapping. Thus, IUP uses both pseudo-range and signal-to-noise measurements to determine position. All algorithms incorporate terrain-height aiding and use measurements from a single epoch in time. Two different 3DMA ranging algorithms are presented, one based on least-squares estimation and the other based on computing the likelihoods of a grid of candidate position hypotheses. The likelihood-based ranging algorithm uses the same candidate position hypotheses as shadow matching and makes different assumptions about which signals are direct line-of-sight (LOS) and non-line-of-sight (NLOS) at each candidate position. Two different methods for integrating likelihood-based 3DMA ranging with shadow matching are also compared. In the position-domain approach, separate ranging and shadow-matching position solutions are computed, then averaged using direction-dependent weighting. In the hypothesis-domain approach, the candidate position scores from the ranging and shadow matching algorithms are combined prior to extracting a joint position solution. Test data was recorded using a u-blox EVK M8T consumer-grade GNSS receiver and a HTC Nexus 9 tablet at 28 locations across two districts of London. The City of London is a traditional dense urban environment, while Canary Wharf is a modern environment. The Nexus 9 tablet data was recorded using the Android Nougat GNSS receiver interface and is representative of future smartphones. Best results were obtained using the likelihood-based 3DMA ranging algorithm and hypothesis-based integration with shadow matching. With the u-blox receiver, the single-epoch RMS horizontal (i.e., 2D) error across all sites was 4.0 m, compared to 28.2 m for conventional positioning, a factor of 7.1 improvement. Using the Nexus tablet, the intelligent urban positioning RMS error was 7.0 m, compared to 32.7 m for conventional GNSS positioning, a factor of 4.7 improvement. An analysis of processing and data requirements shows that intelligent urban positioning is practical to implement in real-time on a mobile device or a server. Navigation and positioning is inherently dependent on the context, which comprises both the operating environment and the behaviour of the host vehicle or user. No single technique is capable of providing reliable and accurate positioning in all contexts. In order to operate reliably across different contexts, a multi-sensor navigation system is required to detect its operating context and reconfigure the techniques accordingly. Specifically, 3DMA GNSS should be selected when the user is in a dense urban environment, not indoors or in an open environment. Algorithms for detecting indoor and outdoor context using GNSS measurements and a hidden Markov model are described and demonstrated

Erosion Based Visibility Preprocessing

International audienceThis paper presents a novel method for computing visibility in 2.5D environments. It is based on a novel theoretical result: the visibility from a region can be conservatively estimated by computing the visibility from a point using appropriately "shrunk" occluders and occludees. We show how approximated but yet conservative shrunk objects can efficiently be computed in a urban environment. The application of this theorem provides a tighter potentially visible set (PVS) than the original method it is built on. Finally, theoretical implications of the theorem are discussed, and we believe it can open new research directions

GNSS Shadow Matching: The Challenges Ahead

GNSS shadow matching is a new technique that uses 3D mapping to improve positioning accuracy in dense urban areas from tens of meters to within five meters, potentially less. This paper presents the first comprehensive review of shadow matching’s error sources and proposes a program of research and development to take the technology from proof of concept to a robust, reliable and accurate urban positioning product. A summary of the state of the art is also included. Error sources in shadow matching may be divided into six categories: initialization, modelling, propagation, environmental complexity, observation, and algorithm approximations. Performance is also affected by the environmental geometry and it is sometimes necessary to handle solution ambiguity. For each error source, the cause and how it impacts the position solution is explained. Examples are presented, where available, and improvements to the shadow-matching algorithms to mitigate each error are proposed. Methods of accommodating quality control within shadow matching are then proposed, including uncertainty determination, ambiguity detection, and outlier detection. This is followed by a discussion of how shadow matching could be integrated with conventional ranging-based GNSS and other navigation and positioning technologies. This includes a brief review of methods to enhance ranging-based GNSS using 3D mapping. Finally, the practical engineering challenges of shadow matching are assessed, including the system architecture, efficient GNSS signal prediction and the acquisition of 3D mapping data

Optimization of PV Modules Layout on High-rise Building Skins Using a BIM-based Generative Design Approach

Growing urbanism and the resulting increase of energy demand coupled with depleting fossil energy resources are making the need for renewable energy resources progressively palpable and vital. In addition to reducing carbon dioxide emissions, renewable energy is crucial to improve health and well-being, and provide affordable energy access worldwide. Photovoltaic (PV) solar energy, as a fast-evolving industry, has become a vital part of the global energy transformation in recent years that can contribute to the development of sustainable cities and the mitigation of global warming. In the urban environment, buildings are central to human activities. Given that buildings currently account for 40% of the global energy consumption, to achieve sustainable urban development, buildings are of particular importance for distributed renewable energy generation, which reduces energy transmission losses. PV panels are able to harvest the solar power and turn it into a clean source of energy. Furthermore, the increasing availability, affordability, and efficiency of PV panels are rendering them an attractive option for the users so that the worldwide use of photovoltaic electricity is growing rapidly by more than 50% a year. Of different types of buildings in the built environment, high-rise buildings are of particular interest because of their high potentials for harvesting a considerable amount of PV energy on vertical and horizontal surfaces. Nevertheless, this high potential is seldom harnessed mainly because the deployment of PV modules on high-rise buildings requires considering a complex interplay between various factors that affect the installation of PV modules (e.g., neighborhood shadow effect, modules self-shadowing effect, surface-specific PV modules, etc.). This renders the design of PV modules in high-rise buildings a complex optimization problem, one that requires a generative design approach. There are many tools and models, from simple 2D evaluation to more comprehensive and complicated 3D analysis, that can help simulate the solar radiation potential of surfaces of a building. However, the majority of the methods do not discriminate between different types of surfaces of the building and treat the entire envelope as a single surface. In recent years, and with the advent and rising popularity of the Building Information Modeling (BIM) concept, the apparatus for the implementation of such a comprehensive generative design approach is becoming increasingly available. However, to the best of the author’s knowledge, there is currently no framework for the BIM-based generative design of PV modules for high-rise buildings. Addressing the current issues, this research aims to: (1) Develop a parametric modeling platform for the design of surface-specific PV module layout on the entire skin of buildings, and (2) Develop a BIM-based generative design framework for the design of PV modules layout on high-rise building skins. In this framework, the surface-specific parametric model of PV modules is integrated with an optimization method to find the optimum design of PV modules layout considering the study period, profit margin, harvested PV energy, and cost. This framework will enable designers and investors to apply the generative design paradigm to the use of PV modules on building skin considering the complex interaction between building surface types (e.g., windows, walls, etc.), type of PV module (e.g., opaque, semi-transparent, etc.), their tilt and pan angles, and the financial aspect of the PV system (i.e., revenue vs. cost at different study periods). The results generated by the elaborate case study demonstrated that the generative design framework is capable of offering more favourable solutions (i.e., either or both of reduced costs and increased energy revenue) compared to baseline scenarios. It is observed that in the majority of the studied scenario, the optimum solutions favored a more consistent orientation of the panels (i.e., consistent pan and tilt angles across all the panels)