ANALISIS LUAS PENGELOLAAN WILAYAH LAUT JAWA TENGAH PADA BEBERAPA SISTEM PROYEKSI DAN SISTEM KOORDINAT

Permendagri No. 141 tahun 2017 menyebutkan bahwa Peta Batas Daerah termasuk Peta Batas Pengelolaan Wilayah Laut menggunakan sistem proyeksi Transverse Mercator dan sistem koordinat UTM. Sistem proyeksi ini bersifat konform yang mempertahankan bentuk (sudut) tetapi tidak mempertahankan luas dan jarak. Penelitian ini menghitung perbedaan luas pada beberapa sistem proyeksi dan sistem koordinat di batas pengelolaan wilayah laut Provinsi Jawa Tengah. Sistem proyeksi dan sistem koordinat yang digunakan adalah: Proyeksi Lambert Silinder Equal Area, Proyeksi UTM Zona 49 Selatan, Proyeksi Transverse Mercator dan Proyeksi Mercator dengan dua sistem koordinat yang berbeda. Selisih luas dihitung terhadap luas hasil proyeksi Lambert Silinder Equal Area yang mempertahankan luas pada sistem proyeksinya. Luas daerah pengelolaan wilayah laut Provinsi Jawa Tengah pada proyeksi UTM dan Transverse Mercator mempunyai persentasi selisih luas yang kecil jika dibandingkan dengan luas proyeksi Lambert Silinder Equal Area. Besarnya persentase selisih luas tidak signifikan sehingga penarikan batas pengelolaan wilayah laut Provinsi Jawa Tengah dapat dilakukan pada sistem proyeksi konform UTM. Perbedaan persentasi luas yang besar pada proyeksi Mercator dibandingkan dengan proyeksi UTM dan Transverse Mercator disebabkan letak daerah yang berjarak paling jauh pada garis yang mempunyai distorsi nol.Permendagri No. 141 of 2017 states that the regional boundary map including the maritime area management boundary map uses the Transverse Mercator projection system and the UTM coordinate system. This projection system is conform projection which maintains shape (angle) but does not maintain area and distance. This study calculates the area differences in several projection systems and coordinate in the management boundaries of the sea territory of The Central Java Province. The projection system and coordinate system used are: Equal Area Cylindrical Lambert Projection, UTM Projection zone 49 south, Transverse Mercator Projection and Mercator Projection with two different coordinate systems. The difference in area is calculated against the projected area of the Lambert Cylindrical Equal Area which maintains the area of the projection system. The area of the sea management area of Central Java Province in the UTM and Transverse Mercator projections has a small percentage difference in area when compared to the projected area of the Lambert Cylindrical Equal Area. The magnitude of the percentage difference in area is not significant so that the drawing of boundaries for the management of the sea area of Central Java Province can be carried out on the UTM conforming projection system. The bigger percentage of area difference of the Mercator projection compared to the UTM and Transverse Mercator projections is due to the location of the area that is the farthest away from the line which has zero distortion

GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization

Worldwide Geo-localization aims to pinpoint the precise location of images taken anywhere on Earth. This task has considerable challenges due to immense variation in geographic landscapes. The image-to-image retrieval-based approaches fail to solve this problem on a global scale as it is not feasible to construct a large gallery of images covering the entire world. Instead, existing approaches divide the globe into discrete geographic cells, transforming the problem into a classification task. However, their performance is limited by the predefined classes and often results in inaccurate localizations when an image's location significantly deviates from its class center. To overcome these limitations, we propose GeoCLIP, a novel CLIP-inspired Image-to-GPS retrieval approach that enforces alignment between the image and its corresponding GPS locations. GeoCLIP's location encoder models the Earth as a continuous function by employing positional encoding through random Fourier features and constructing a hierarchical representation that captures information at varying resolutions to yield a semantically rich high-dimensional feature suitable to use even beyond geo-localization. To the best of our knowledge, this is the first work employing GPS encoding for geo-localization. We demonstrate the efficacy of our method via extensive experiments and ablations on benchmark datasets. We achieve competitive performance with just 20% of training data, highlighting its effectiveness even in limited-data settings. Furthermore, we qualitatively demonstrate geo-localization using a text query by leveraging CLIP backbone of our image encoder. The project webpage is available at: https://vicentevivan.github.io/GeoCLIPComment: Accepted at NeurIPS 202

IMPLEMENTASI ALGORITME A-STAR UNTUK PEMETAAN KOORDINAT TUMBUHAN LANGKA BERBASIS WEB

The path finding optimization is the most widely discussed issues in the informatics scope. This was related to the increased needs of the transportation, distribution and industry. The method for solving search problems for the shortest path can be done using two methods, namely the conventional method and the heuristic method. The conventional method uses a mathematical approach that is easy to understand, but the search results require a relatively long time. In order for search time to be faster, a heuristic approach is needed, although it requires more parameters. A-star algorithm is a heuristic route search method that is very effective in finding the shortest route. In this study a web-based application was developed by integrating the A-star algorithm to find the location of rare plants in the conservation area. As for determining the distance between the coordinates of the plant using haversine formula. This application is very important because of the large number of plants and the limited guidance of researchers in finding the location of rare plants in conservation areas. The guide is presented in the form of an easy-to-understand graph accompanied by information about the plants sought

Warming-driven erosion and sediment transport in cold regions

We synthesized a global inventory of cryosphere degradation-driven increases in erosion and sediment yield, e.g., suspended load, bedload, particulate organic carbon, and riverbank/slope erosion. This inventory includes 76 locations from the high Arctic, European mountains, High Mountain Asia and Andes, and 18 Arctic permafrost-coastal sites, and they were collected from ~80 studies

The Mercator Projection: its uses, misuses, and its association with scientific information and the rise of scientific societies

This study examines the uses and misuses of the Mercator Projection for the past 400 years. In 1569, Dutch cartographer Gerard Mercator published a projection that revolutionized maritime navigation. The Mercator Projection is a rectangular projection with great areal exaggeration, particularly of areas beyond 50 degrees north or south, and is ill-suited for displaying most reference and thematic world maps. The current literature notes the significance of Gerard Mercator, the Mercator Projection, the general failings of the projection, and the twentieth century controversies that arose as a consequence of its misuse. This dissertation illustrates the path of the institutionalization of the Mercator Projection in western cartography and the roles played by navigators, scientific societies and agencies, and by the producers of popular reference and thematic maps and atlases. The data are pulled from the publication record of world maps and world maps in atlases for content analysis. The maps ranged in date from 1569 to 1900 and displayed global or near global coverage. The results revealed that the misuses of the Mercator Projection began after 1700, when it was connected to scientists working with navigators and the creation of thematic cartography. During the eighteenth century, the Mercator Projection was published in journals and reports for geographic societies that detailed state-sponsored explorations. In the nineteenth century, the influence of well-known scientists using the Mercator Projection filtered into the publications for the general public. This dissertation offers a glimpse into the complexities of mapping, the choice of map projection and why the Mercator Projection changed human’s ability of moving from one place to another, or, their perception of spatial arrangement of the globe

Cartografía y Geometría

This work deals with one of the oldest problems in geometry, the construction of maps of the Earth. Although we know that does not have a perfectly spherical shape, we will simplify the problem approximating it by a sphere of radius 1.Universidad de Granada. Facultad de Ciencias. Grado en Matemáticas. Curso académico 2021-202

Landslide size matters: a new data-driven, spatial prototype

The standard definition of landslide hazard requires the estimation of where, when (or how frequently) and how large a given landslide event may be. The geoscientific community involved in statistical models has addressed the component pertaining to how large a landslide event may be by introducing the concept of landslide-event magnitude scale. This scale, which depends on the planimetric area of the given population of landslides, in analogy to the earthquake magnitude, has been expressed with a single value per landslide event. As a result, the geographic or spatially-distributed estimation of how large a population of landslide may be when considered at the slope scale, has been disregarded in statistically-based landslide hazard studies. Conversely, the estimation of the landslide extent has been commonly part of physically-based applications, though their implementation is often limited to very small regions. In this work, we initially present a review of methods developed for landslide hazard assessment since its first conception decades ago. Subsequently, we introduce for the first time a statistically-based model able to estimate the planimetric area of landslides aggregated per slope units. More specifically, we implemented a Bayesian version of a Generalized Additive Model where the maximum landslide size per slope unit and the sum of all landslide sizes per slope unit are predicted via a Log-Gaussian model. These “max” and “sum” models capture the spatial distribution of (aggregated) landslide sizes. We tested these models on a global dataset expressing the distribution of co-seismic landslides due to 24 earthquakes across the globe. The two models we present are both evaluated on a suite of performance diagnostics that suggest our models suitably predict the aggregated landslide extent per slope unit. In addition to a complex procedure involving variable selection and a spatial uncertainty estimation, we built our model over slopes where landslides triggered in response to seismic shaking, and simulated the expected failing surface over slopes where the landslides did not occur in the past. What we achieved is the first statistically-based model in the literature able to provide information about the extent of the failed surface across a given landscape. This information is vital in landslide hazard studies and should be combined with the estimation of landslide occurrence locations. This could ensure that governmental and territorial agencies have a complete probabilistic overview of how a population of landslides could behave in response to a specific trigger. The predictive models we present are currently valid only for the 25 cases we tested. Statistically estimating landslide extents is still at its infancy stage. Many more applications should be successfully validated before considering such models in an operational way. For instance, the validity of our models should still be verified at the regional or catchment scale, as much as it needs to be tested for different landslide types and triggers. However, we envision that this new spatial predictive paradigm could be a breakthrough in the literature and, in time, could even become part of official landslide risk assessment protocols

GPU-accelerated 3D visualisation and analysis of migratory behaviour of long lived birds

With the amount of data we collect increasing, due to the efficacy of tagging technology improving, the methods we previously applied have begun to take longer and longer to process. As we move forward, it is important that the methods we develop also evolve with the data we collect. Maritime visualisation has already begun to leverage the power of parallel processing to accelerate visualisation. However, some of these techniques require the use of distributed computing, that while useful for datasets that contain billions of points, is harder to implement due to hardware requirements. Here we show that movement ecology can also significantly benefit from the use of parallel processing, while using GPGPU acceleration to enable the use of a single workstation. With only minor adjustments, algorithms can be implemented in parallel, enabling for computation to be completed in real time. We show this by first implementing a GPGPU accelerated visualisation of global environmental datasets. Through the use of OpenGL and CUDA, it is possible to visualise a dataset containing over 25 million datapoints per timestamp and swap between timestamps in 5ms, allowing for environmental context to be considered when visualising trajectories in real time. These can then be used alongside different GPU accelerated visualisation methods, such as aggregate flow diagrams, to explore large datasets in real time. We also continue to apply GPGPU acceleration to the analysis of migratory data through the use of parallel primitives. With these parallel primitives we show that GPGPU acceleration can allow researchers to accelerate their workflow without the need to completely understand the complexities of GPU programming, allowing for orders of magnitude faster computation times when compared to sequential CPU methods

L'analisi spaziale

The so-called data deluge, along with ever-increasing technological capabilities, tantalizes geographers with exciting opportunities for spatial data analysis. These opportunities come with challenges, because data and technology, alone, cannot address the pressing questions of our world. Spatial analysis, a.k.a. spatial statistics, is a lot more than colourful maps and attractive displays: still relatively underrepresented in the Italian geography, this discipline has grown from a strictly quantitative niche to part of a critical spatial science and continues to stimulate new developments in statistics because, as we know, spatial is special. This book, published in the series “New Geographies. Work Tools”, adds spatial analysis to the Italian geographer’s toolbox. Not a how-to manual, it presents some of the core analytical issues through the redundancy of narrative language and mathematical language. It traces the journey of spatial analysis from its roots in quantitative geography, GIS, and statistics, towards the definition of its own identity and the acceptance of its own relativity and limitations. It discusses the relationship of spatial analysis with GIScience and its efforts to embed critiques within its own discourse, emphasizing the role of theory, the importance of hypothesis testing, and acknowledging the ethics surrounding the use and analysis of data. A few examples illustrate practical implementations, showing the value added by spatial statistics in yielding reliable analyses that can support management decisions. It concludes with a brief outlook on the Italian geographic literature, where spatial analysis – like elsewhere – can play a role in competently accepting today’s opportunities and challenges, in a constructive dialogue within geography as a whole