Infrastructure Mapping Research Articles

Building Extraction for Urban Infrastructure Mapping Using Deep Neural Networks and High-Resolution Remote Sensing Data

Building Extraction for Urban Infrastructure Mapping Using Deep Neural Networks and High-Resolution Remote Sensing Data

Mapping the Green Urban: A Comprehensive Review of Materials and Learning Methods for Green Infrastructure Mapping.

Green infrastructure (GI) plays a crucial role in sustainable urban development, but effective mapping and analysis of such features requires a detailed understanding of the materials and state-of-the-art methods. This review presents the current landscape of green infrastructure mapping, focusing on the various sensors and image data, as well as the application of machine learning and deep learning techniques for classification or segmentation tasks. After finding articles with relevant keywords, the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyzes) method was used as a general workflow, but some parts were automated (e.g., screening) by using natural language processing and large language models. In total, this review analyzed 55 papers that included keywords related to GI mapping and provided materials and learning methods (i.e., machine or deep learning) essential for effective green infrastructure mapping. A shift towards deep learning methods can be observed in the mapping of GIs as 33 articles use various deep learning methods, while 22 articles use machine learning methods. In addition, this article presents a novel methodology for automated verification methods, demonstrating their potential effectiveness and highlighting areas for improvement.

Assessing the efficacy of artificial intelligence based city-scale blue green infrastructure mapping using Google Earth Engine in the Bangkok metropolitan region

Urbanization disrupts natural water, energy, and nutrient cycles, but integrating green and blue infrastructures (BGI) can mitigate these effects by facilitating processes like evapotranspiration, soil water infiltration, and root nutrient absorption. In Bangkok Metropolitan Region (BMR), escalating urbanization poses challenges to these natural cycles. Mapping Land Use and Land Cover (LULC) and identifying green infrastructure locations are crucial for effective urban planning, sustainable development, and environmental conservation amidst rapid urban growth. Continuous monitoring of dynamic urban areas is time-consuming, labor-intensive, and costly. Previous research primarily focused on land use and land cover classification followed by BGI identification. An automated classification system focusing solely on BGI can greatly enhance efficiency, accuracy, and cost-effectiveness in land classification for urban planning and decision-making. However, automating this system remains a significant challenge for the remote sensing community. Therefore, the research is first to develop a cloud based artificial intelligence tools such as Smile Random Forest and Smile CART integration with Google Earth Engine (GEE) to identify BGI for BMR. For this analysis, we have used open-sources and mostly used satellite images (e.g., Landsat and Sentinel) and consider to analyze seasonal changes for waterbodies, natural vegetations and human intervened vegetations with developing cloud-based artificial intelligence (AI). Surprisingly, Landsat-9 data demonstrated superior accuracy compared to Sentinel-2, indicating that the advanced technology of Landsat 9 may be more effective for BGI classification using AI. The study revealed a most distinct transition from deep green to green infrastructures during the transition from summer to monsoon season, whereas significant changes in blue infrastructure occurred between the monsoon and winter seasons. Seasonal variations in BGI are complex and influenced by factors such as the types of BGI implemented and the nuances of local climatic conditions. These advancements could provide precise insights for urban managers and policymakers, offering valuable tools to identify and understand BGI dynamics across various urban scales.

Green Infrastructure Mapping in Almeria Province (Spain) Using Geographical Information Systems and Multi-Criteria Evaluation

Green infrastructure (GI) is increasingly prioritised in landscape policy and planning due to its potential to benefit ecosystems and enhance wildlife conservation. However, due to the uneven distribution of protected areas (PAs) and the fragmentation of habitats more generally, multi-level policy strategies are needed to create an integrated GI network bridging national, regional and local scales. In the province of Almeria, southeastern Spain, protected areas are mainly threatened by two land use/land cover changes. On the one hand, there is the advance of intensive greenhouse agriculture, which, between 1984 and 2007, increased in surface area by more than 58%. On the other hand, there is the growth of artificial surfaces, including urban areas (+64%), construction sites (+194%) and road infrastructures (+135%). To address this challenge, we present a proposal for green infrastructure deployment in the province of Almeria. We combine Geographic Information Systems (GISs) and multi-criteria evaluation (MCE) techniques to identify and evaluate suitability for key elements to be included in GI in two key ways. First, we identify the most suitable areas to form part of the GI in order to address vulnerability to degradation and fragmentation. Second, we propose 15 ecological corridors connecting the 35 protected areas of the province that act as core areas. The proposed GI network would extend along the western coast of the province and occupy the valleys of the main rivers. The river Almanzora plays a leading role. Due to its remoteness from the coast and its climatic conditions, it has not attracted intensive greenhouse agriculture and urban development, the main drivers of the transformation and fragmentation of traditional land uses. Around 50% of the area occupied by the proposed corridors would be located in places of medium and high suitability for the movement of species between core areas.

Russia’s Mapping of Critical Infrastructure in the North and Baltic Seas – International Law as an Impediment to Countering the Threat of Strategic Sabotage?

Abstract Russia is systematically mapping critical infrastructure in the North and Baltic Seas. These activities are intended to unsettle nato countries and prepare the ground for possible sabotage. The problem is that the international law of the sea does not provide coastal States with clear authority to prevent the collection of intelligence on maritime infrastructure within their Exclusive Economic Zones (eez s). This article argues that coastal States must nevertheless be able to exercise their sovereign rights with respect to the exploration and exploitation of the eez and continental shelf. Consequently, they must be allowed to take the necessary measures to protect the infrastructure serving the exercise of those sovereign rights. This argument could help to establish a legal basis for countering Russian mapping operations in the eez s and on the continental shelves of coastal States in the North and Baltic Seas.

Mapping the European Union’s Renewable Energy Transition Amidst the Russo-Ukrainian War

This research paper delves into the complex interplay between the European Union's (EU) ambitious renewable energy transition and the geopolitical backdrop of the Russo-Ukrainian War. Against the backdrop of evolving global energy dynamics and the EU's commitment to sustainability, the study aims to elucidate how the conflict has shaped and influenced the EU's pursuit of renewable energy sources. The Russo-Ukrainian War, marked by geopolitical tensions and energy security concerns, has prompted the EU to reassess its energy landscape. The paper employs a multi-faceted approach, combining geopolitical analysis, policy evaluation, and energy infrastructure mapping to understand how the conflict has impacted the EU's renewable energy strategies. It scrutinises the policy shifts, regulatory frameworks, and investment patterns in the renewable energy sector within the EU member states. Furthermore, the research investigates the role of energy diplomacy and the EU's efforts to diversify its energy sources in response to the geopolitical instability stemming from the conflict. It explores the challenges and opportunities presented by the war, such as the urgency for energy independence and the need for cohesive cross-border collaboration. By employing data-driven analyses and case studies, this paper contributes to the broader discourse on the intersection of geopolitics and renewable energy transitions. It offers insights into the dynamic relationship between regional conflicts and the imperative for sustainable energy practices, providing policymakers, scholars, and industry stakeholders with valuable perspectives on navigating the intricate landscape of renewable energy transitions in the midst of geopolitical uncertainties.

Pemetaan Infrastruktur Fisik Desa Sebagai Kampung Wisata Bunga di Banyuurip Kec. Kedamean, Kabupaten Gresik

Tourism is one of the sectors that collapsed during the COVID-19 pandemic. On the other hand, floral plants have increasing turnover along with Working From Home. So the Tourism Village with floral plant-based is one solution to improve the local economy during the COVID-19 pandemic. This research aims to create physical infrastructure mapping at Banyuurip, Kedamean Sub-district, Gresik District to embody Floral Tourism Village. This method uses descriptive qualitative by describing the survey and mapping results to provide an overview of the existing physical infrastructure of Banyuurip Village. The results of this research indicate that sub-Village of Miru and Pendem are centers of the Floral Tourism Village in Banyuurip which are supported by adequate eating facilities, transportation support, telecommunications, water systems, and electricity networks.

Automated Built-Up Infrastructure Land Cover Extraction Using Index Ensembles with Machine Learning, Automated Training Data, and Red Band Texture Layers

Automated built-up infrastructure classification is a global need for planning. However, individual indices have weaknesses, including spectral confusion with bare ground, and computational requirements for deep learning are intensive. We present a computationally lightweight method to classify built-up infrastructure. We use an ensemble of spectral indices and a novel red-band texture layer with global thresholds determined from 12 diverse sites (two seasonally varied images per site). Multiple spectral indexes were evaluated using Sentinel-2 imagery. Our texture metric uses the red band to separate built-up infrastructure from spectrally similar bare ground. Our evaluation produced global thresholds by evaluating ground truth points against a range of site-specific optimal index thresholds across the 24 images. These were used to classify an ensemble, and then spectral indexes, texture, and stratified random sampling guided training data selection. The training data fit a random forest classifier to create final binary maps. Validation found an average overall accuracy of 79.95% (±4%) and an F1 score of 0.5304 (±0.07). The inclusion of the texture metric improved overall accuracy by 14–21%. A comparison to site-specific thresholds and a deep learning-derived layer is provided. This automated built-up infrastructure mapping framework requires only public imagery to support time-sensitive land management workflows.

A multi-objective comparison of CNN architectures in Arctic human-built infrastructure mapping from sub-meter resolution satellite imagery

ABSTRACTRisk assessment of infrastructure exposed to ice-rich permafrost hazards is essential for climate change adaptation in the Arctic. As this process requires up-to-date, comprehensive, high-resolution maps of human-built infrastructure, gaps in such geospatial information and knowledge of the applications required to produce it must be addressed. Therefore, this study highlights the ongoing development of a deep learning approach to efficiently map the Arctic built environment by detecting nine different types of structures (detached houses, row houses, multi-story blocks, non-residential buildings, roads, runways, gravel pads, pipelines, and storage tanks) from recently-acquired Maxar commercial satellite imagery (<1 m resolution). We conducted a multi-objective comparison, focusing on generalization performance and computational cost, of nine different semantic segmentation architectures. K-fold cross validation was used to estimate the average F1-score of each architecture and the Friedman Aligned Ranks test with the Bergmann-Hommel post-hoc procedure was applied to test for significant differences in generalization performance. ResNet-50-UNet++ performs significantly better than five out of the other eight candidate architectures; no significant difference was found in the pairwise comparisons of ResNet-50-UNet++ to ResNet-50-MANet, ResNet-101-MANet, and ResNet-101-UNet++. We then conducted a high-performance computing scaling experiment to compare the number of service units and runtime required for model inferencing on a hypothetical pan-Arctic scale dataset. We found that the ResNet-50-UNet++ model could save up to ~ 54% on service unit expenditure, or ~ 18% on runtime, when considering operational deployment of our mapping approach. Our results suggest that ResNet-50-UNet++ could be the most suitable architecture (out of the nine that were examined) for deep learning-enabled Arctic infrastructure mapping efforts. Overall, our findings regarding the differences between the examined CNN architectures and our methodological framework for multi-objective architecture comparison can provide a foundation that may propel future pan-Arctic GeoAI mapping efforts of infrastructure.

Assessment of feasible site locations for biofuel production based on technoeconomic modelling and GHG impact analysis

Large scale bioenergy is expected to play an increasing role in the industry, heat and power production and transportation in the future. Both biomass availability and cost-effective mobilization are necessary to facilitate large bioenergy production sites. This study uses a Geographical Information System approach to map the economic and environmental feasibility of future biofuel production sites via Hydrothermal Liquefaction. The methodology includes process modelling, biomass and infrastructure mapping, technoeconomic analysis and greenhouse gas impact assessment and is implemented having Denmark as case study. Three supply-chains were evaluated for the upgrading of the biofuel which are chemical stabilizing, on-site hydrotreating, and centralised hydrotreating. The two feedstocks assessed were imported forestry and domestic agricultural residue resulting in a total of six different implementation scenarios. The results for the case study indicate that for forestry residue the proximity to an industrial port is the most dominating factor when determining feasible site locations. The performance in the agricultural residue scenarios is more impacted by infrastructure parameters. In the on-site hydrotreating scenario the best performing locations are found close to the hydrogen line to reduce connection expenses. For centralised hydrotreating the results favour being close to existing refineries to reduce intermediate transportation of the biocrude.

PEMETAAN PRASARANA DAN KONTUR KAMPUS UNIVERSITAS WIRARAJA BERDASARKAN CITRA PENGINDRAAN JAUH

Wiraraja University campus from its establishment in 1986 to 2023 has a period of 37 years which has experienced many developments, especially learning infrastructure. The development of infrastructure needs to be mapped to determine the distribution of learning infrastructure on the Wiraraja University campus. The utilization of geographic information system (GIS) technology and the development of infrastructure on the Wiraraja University campus, it is necessary to map the infrastructure and contours on the Wiraraja University campus which will later obtain an overview of the infrastructure. The purpose of the study was to determine the infrastructure map and the percentage of infrastructure to the overall land and contour map of Wiraraja University campus. The research used Google Earth and ArcMap. The research data collection was carried out by field survey and data collection of infrastructure on the campus of Wiraraja University and the research data was carried out by frequency analysis.&#x0D; Land use on the campus of Wiraraja University is divided into the categories of buildings, sports fields, covered and open parking, parks, roads and vacant land. The land area of Wiraraja University campus based on Google Earth images with the digitization method is 22,711.86 m2. The largest land use is used as a building at 37.54%, while the smallest land use is used as a covered parking lot at 1.78%. The elevation of the Wiraraja University campus is between 3.67 - 5.94 m elevation and the highest elevation is 5.94 meters and the lowest elevation is 3.67 meters above mean sea level.

Development of a compact pulsed time-of-flight LiDAR platform for underwater measurements

The mapping and inspection of underwater topography and infrastructure require precise and robust instrumentation which cannot be completely fulfilled by sonar or camera-based systems. At Fraunhofer IPM we have therefore built a versatile platform that implements all necessary building blocks for various laser scanner configurations targeted at subsea applications. Based on this platform, we present two scanner prototypes: One compact and lightweight multi-wavelength airborne laser scanner for deployment with unmanned aerial vehicles. It is intended for easy and fast shallow water bathymetric and topographic mapping. The second scanner is embedded in a pressure resistant housing and designed for submerged deployment on stationary or moving platforms to facilitate numerous inspection tasks. Initial tests of the scanners have been performed in an excavation pond and on a dedicated underwater measurement range. The resulting point clouds indicate promising performance in terms of resolution, accuracy, and speed. Further work will focus on extensive field tests and software optimization for increased usability.

A mobile mapping solution for VRU Infrastructure monitoring via low-cost LiDAR-sensors

ABSTRACT LiDAR scanning is a widely-used method for accurately assessing roads and vehicular infrastructure. However, it can be costly and often overlooks infrastructure important for Vulnerable Road Users (VRU) like pedestrians and cyclists. Advances in electronics now permit high-performance LiDAR sensors made of standard semiconductor materials to be integrated into personal devices like smartphones and tablets. This study introduces a new classification category of handheld LiDAR systems for the purpose of VRU infrastructure mapping on a smaller scale. Qualitative experiments using a handheld LiDAR system were conducted to achieve low-cost mobile mapping of VRU infrastructure. The 3D Scanner App facilitated detailed and qualitative point cloud generation at low speeds. Moreover, it demonstrated precise detection of perpendicular potholes and bumps on roads. A quantitative analysis based on several infrastructure damages was performed, revealing mean average percentage errors of 3.53%, 4.43%, and 10.57% in the x-, y-, and z-directions, respectively. While the accuracy in the x- and y-directions is acceptable, the higher z-direction error indicates overestimation of infrastructural damage depth. In conclusion, the sensor's capabilities enable cost-effective mobile mapping solutions for VRU infrastructure monitoring, such as crowd-sourcing for urban data mining and urban digital twin interfaces, significantly reducing expenses compared to traditional sensors.

Mapping and assessment of coastal infrastructure for adaptation to coastal erosion along the coast of Ghana

Globally, coastal managers are challenged to make informed decisions when selecting coastal infrastructure to respond to climate-induced sea-level rise and associated coastal hazards like coastal erosion and flooding. Classifying the types of coastal infrastructure permits the comparison of their potential efficiency, environmental and socioeconomic impacts, and long-term response to sea-level rise. At present, information on coastal infrastructure implemented along the coastal area of Ghana is not known thus creating a research gap to catalog this information. To achieve this, we combined satellite images from Google Earth Pro and the use of ArcGIS capabilities to conduct a national assessment of coastal infrastructure and its distribution along the coast of Ghana. Even though similar approaches have been applied in different geographic contexts, this article focuses on evaluating coastal infrastructure in Ghana. Results show that between 2004 and 2022, at least 110 km or approximately 20% of the coast of Ghana has been protected using grey infrastructure, distributed as groins 35.9 km (6.5%), revetments and seawalls 50 km (9%), and jetties and port breakwaters 25 km (4.5%) of the 550 Km coastline. These do not include the numerous private recreational infrastructure that could increase coastal vulnerability. The increasing use of grey infrastructure, particularly seawalls, and revetments along the coast has adverse impacts on overall coastal evolution and causes socioeconomic challenges. This study supports coastal managers to review coastal adaptation policy and develop shoreline management plans for the coast of Ghana.

Mapping of familial hypercholesterolemia and dyslipidemias basic management infrastructure in Pakistan: a cross-sectional study.

Familial hypercholesterolemia (FH) is an autosomal inherited disorder characterised by elevated low-density lipoprotein cholesterol and premature cardiovascular events. Despite being declared as a public health priority, FH remains highly underdiagnosed, generally due to the lack of awareness and shortcomings in the available infrastructure, particularly in lower income countries. To map the existing infrastructure for the management of FH, a survey was conducted among 128 physicians (cardiologists, paediatricians, endocrinologists, and internal medicine specialists) from different regions of Pakistan. The respondents encountered a limited number of adults or children with diagnosed FH. A very small proportion of the population had access to free cholesterol and genetic testing even when indicated by a physician. In general, cascade screening of the relatives was not performed. Uniform diagnostic criteria for FH had not been established even within the same institution or province. The use of statins and ezetimibe in addition to lifestyle changes were the most common recommended treatment option for FH patients. The respondents considered lack of financial resources as a major barrier for the management of FH and stressed on taking relevant measures for a uniform FH screening programs around the country. National FH screening programmes are not in place worldwide hence FH is commonly undiagnosed, and many individuals are at a high risk for cardiovascular diseases. Timely screening of population for FH requires knowledge about FH among the clinicians and the availability of fundamental infrastructure coupled with sufficient financial resources. The authors confirm independence from the sponsor. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. FS received funding from Higher Education Commission, Pakistan (Grant 20-15760) and UG received grants from Slovenian Research Agency (J3-2536, P3-0343).

Review of Artificial Intelligence Applications for Virtual Sensing of Underground Utilities.

Accurately identifying the location and depth of buried utility assets became a considerable challenge in the construction industry, for which accidental strikes can cause important economic losses and safety concerns. While the collection of as-built utility locations is becoming more accurate, there still exists an important need to be capable of accurately detecting buried utilities in order to eliminate risks associated with digging. Current practices typically involve the use of trained agents to survey and detect underground utilities at locations of interest, which is a costly and time-consuming process. With advances in artificial intelligence (AI), an opportunity arose in conducting virtual sensing of buried utilities by combining robotics (e.g., drones), knowledge, and logic. This paper reviewed methods that are based on AI in mapping underground infrastructure. In particular, the use of AI in aerial and terrestrial mapping of utility assets was reviewed, followed by a summary of AI techniques used in fusing multi-source data in creating underground infrastructure maps. Key observations from the consolidated literature were that (1) when leveraging computer vision methods, automatic mapping techniques vastly focus on manholes localized from aerial imagery; (2) when applied to non-intrusive sensing, AI methods vastly focus on empowering ground-penetrating radar (GPR)-produced data; and (3) data fusion techniques to produce utility maps should be extended to any utility assets/types. Based on these observations, a universal utility mapping model was proposed, one that could enable mapping of underground utilities using limited information available in the form of different sources of data and knowledge.

Разработка способов представления на картах инфраструктуры для средств индивидуальной мобильности в г. Москве

The number of cyclists and users of personal mobility aids in Moscow is growing every year, while building the necessary facilities, providing security and managing the cycling network has not always kept pace with such rapid growth. The construction of infrastructure for means of individual mobility involves planning and designing it depending on the places of attraction of target audiences (universities, transport hubs, parks and the most popular streets for cyclists). At the same time, a cartographic vision of the problem is necessary at all stages. A review of works on the possibilities of depicting cycling infrastructure and the movement of cyclists on the streets of the city shows little experience in presenting this topic on maps. The main function of existing maps is to display the actual position of linear and point objects of cycling without a spatial analysis of their position. The article shows the possibilities of infrastructure mapping for means of individual mobility and solves the problem of finding reliable and up-to-date sources of statistical and cartographic data. The information collected in the course of urban and field surveys of the urban space of Moscow was processed and systematized. A database of bicycle infrastructure objects has been formed, which includes vector and raster representations of data, the results of field and statistical studies. Based on it, methods for visualizing the obtained data have been developed. The paper formulates the basic requirements for the cartographic basis for creating infrastructure maps, shows various techniques and ways of depicting phenomena, which made it possible to clearly demonstrate the results of the study. Considering the visualization possibilities found, maps of the cycling infrastructure were compiled, highlighting its main elements and types.

Geographic Information System Mapping of Public Works Infrastructure of Kebumen Regency

Geographic Information System (GIS) is an information system that combines graphical (visual) data with text data (attributes) of geographically referenced objects on the Earth's surface. GIS is widely used today in various fields, including analysis, industries, and individual activities such as finding locations or routes to specific destinations. In this research, GIS is used to manage the digitization of infrastructure mapping in Kebumen Regency. The development objective of the GIS is to process and analyze the spatial data of infrastructure locations to generate accurate information. The waterfall method is employed in the development of this system, encompassing stages of analysis, design, implementation, testing, and maintenance using the CodeIgniter framework. The outcome of this research is the Geographic Information System for Mapping Public Works Infrastructure in Kebumen Regency based on Leaflet. The GIS infrastructure of Kebumen Regency facilitates the public in accessing information about the existing infrastructure in the area. The testing results using the black box testing method demonstrate that the system aligns with the defined requirements. The objective of this research is to develop a Geographic Information System (GIS) that can manage and analyze the spatial data of infrastructure in Kebumen Regency, resulting in accurate information. The waterfall method is used in the development of this system, with stages including analysis, design, implementation, testing, and maintenance using the CodeIgniter framework. The outcome of this research is the Geographic Information System for Mapping Public Works Infrastructure in Kebumen Regency based on Leaflet. With the GIS infrastructure of Kebumen Regency, the community can easily access information regarding the infrastructure in the area. This system enables users to visualize the locations of infrastructure and obtain attribute or text data related to those infrastructure objects. Through testing using the black box testing method, the system has been proven to meet the predefined requirements. This indicates that the developed GIS is reliable and provides accurate information about the infrastructure in Kebumen Regency. Thus, the development of this GIS provides significant benefits in managing spatial data of infrastructure and delivering accurate information to the public. This research has successfully achieved its objectives and produced a Geographic Information System that can be utilized as a tool for infrastructure mapping and spatial data analysis in Kebumen Regency.

On Improving the Accuracy of Internet Infrastructure Mapping

On Improving the Accuracy of Internet Infrastructure Mapping

PKM BADAN PELESTARIAN PUSAKA INDONESIA (BPPI) DALAM PEMETAAN KAWASAN SUBAK SELAT

One of the interesting natural attractions to visit in Bali is Balinese terraced rice fields and an agricultural irrigation system known as subak. Subak as an irrigation system, the subak system is continuously undergoing a transformation process because its environment influences it. UNESCO recognition is Cultural Landscape of Bali Province: Subak as Manifestation of Tri Hita Karana Philosophy. The meaning of UNESCO's recognition is that the philosophy of Tri Hita Karana (THK) is developing in Bali. At present, the rice field area managed by subak has decreased due to land conversion. Several rice fields have now changed their function to become residential, industrial, tourist facilities, and other uses. As an effort to prevent the degradation of subak conditions, the government has made various efforts but has not been able to overcome land conversion. This effort must be assisted by all elements of society so that subak consistency is maintained in Bali. One agency that focuses on subak conservation efforts is the Indonesian Heritage Preservation Agency (BPPI). BBPI routinely conducts subak schools through the Bali International Field School for Subak (BIFSS) activities. BBPI as a partner in this PKM publishes various books related to subak conservation. Education about subak is one of the efforts to preserve subak. The implementation of this collaboration involves FTP's involvement in the BIFSS 2021 activities, which will take place in December 2021 in Subak Selat, Karangasem Regency. From the discussions carried out, in an effort to preserve subak, a Map of the Subak Strait Area is needed, including mapping of paddy fields and irrigation infrastructure. This map forms the basis for the strategy for preserving and developing the Subak Strait as a tourist attraction as well as efforts to increase the value of Subak.