AI

GenAI

The Role of Generative AI in Transforming Programming Practices

By • • Research, Teaching • , , • 5 minutes of reading

Generative AI (GenAI) has emerged as a transformative tool in the field of software development, extending its capabilities beyond text generation to the creation of computer code. This advancement aligns with the understanding that computer code is essentially another form of language, making it possible for AI models to aid developers in their work. GenAI can accelerate various programming tasks, thereby enhancing the efficiency of software development. Its ability to convert natural language instructions into executable code and provide real-time code suggestions has the potential to reshape the role of developers in the industry. However, the question remains: how effective […]

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Peranan AI dalam Pembangunan Perisian dan Aplikasi

By • • Project, Public, Teaching, Uncategorized • , , , , , , • 6 minutes of reading

Oleh Shahabuddin Amerudin Kecerdasan Buatan (AI) kini menjadi salah satu teknologi teras dalam pembangunan perisian dan aplikasi, membawa revolusi dalam cara perisian dibina, diuji, dan diselenggara. Dengan kemajuan terkini dalam pembelajaran mesin, automasi, dan pemprosesan bahasa semula jadi (NLP), AI membantu mempercepatkan pembangunan kod, meningkatkan kecekapan pengujian perisian, dan memudahkan integrasi analitik pintar ke dalam aplikasi. Namun, penggunaan teknologi ini juga datang dengan cabaran, termasuk isu keselamatan, kebergantungan pada platform tertentu, dan potensi risiko kebergantungan kepada alat AI yang terlalu tinggi. Artikel ini akan mengupas bagaimana AI membantu dalam proses pembangunan perisian serta alat-alat terkini yang boleh digunakan, dengan memberi

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Open Data and AI for Environmental Justice: Insights and Implications

By • • Other, Public, Research • , , , • 4 minutes of reading

By Shahabuddin Amerudin The article from Geospatial World highlights an interview with Amen Ra Mashariki, Director of AI and Data Strategies at the Bezos Earth Fund, on the intersection of open data, AI, and environmental justice (Geospatial World, 2024). The interview covers a wide range of topics, including the importance of open data for equitable climate solutions, the role of AI in processing large datasets, and ethical considerations in the geospatial domain. This review article will critically examine the perspectives presented, focusing on the practical applications and challenges of using open data and AI for environmental justice. Understanding Environmental Justice and Open

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GeoAI and planning

Advancing Urban Planning with GeoAI through Global Street Network Analysis

By • • Jurnal, Knowledge, Project, Research, Teaching • , , , , • 7 minutes of reading

By Shahabuddin Amerudin Introduction Geographic Artificial Intelligence (GeoAI) integrates Geographic Information Systems (GIS) with artificial intelligence (AI), offering advanced capabilities for urban planning and development. This convergence allows for a more nuanced understanding of spatial dynamics and provides tools to address complex urban challenges. By harnessing GeoAI, urban planners can optimize infrastructure, manage resources more efficiently, and create sustainable urban environments. This article delves into how GeoAI can be applied to enhance city planning by analyzing street network configurations across different global cities. Understanding GeoAI GeoAI represents the intersection of spatial data analysis and AI technologies, including machine learning and

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Revolutionizing Geospatial Data Analysis Through Generative AI

By • • Knowledge, Public • , , • 4 minutes of reading

Introduction In recent years, Generative Artificial Intelligence (AI) has emerged as a revolutionary force in various industries, transforming the way data is analyzed, interpreted, and leveraged for actionable insights. Nowhere is this transformation more evident than in the realm of geospatial data analysis. The integration of Generative AI into the analysis of sensor and machine datasets has ushered in a new era of efficiency, accuracy, and innovation. This article explores the groundbreaking role of Generative AI in geospatial analytics and its ability to simplify complex tasks while exponentially increasing productivity. Generative AI Unleashes the Power of Geospatial Data Geospatial data

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Unveiling the Power of Geospatial Artificial Intelligence (GeoAI) and its Applications

By • • Knowledge, Project, Research, Teaching • , , , • 6 minutes of reading

By Shahabuddin Amerudin Introduction The term Geospatial Artificial Intelligence (GeoAI) lacks a universally agreed-upon definition. Initially, GeoAI referred to the utilisation of machine learning tools within Geographic Information Systems (GISs) to predict future scenarios by classifying data. This included disaster occurrence, human health epidemiology, and ecosystem evolution, aimed at bolstering community resilience through traditional geographic information in digital cartography (Esri, 2018). A broader interpretation considers GeoAI as processing Geospatial Big Data (GBD) encompassing various sources, such as digital cartography, remote-sensing-based multidimensional data, and georeferenced texts. The focal point is the geographic dimension (Janowicz et al., 2019. Thus, GeoAI merges AI

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The Dynamic Potential of Named Entity Recognition (NER) in Extracting and Analyzing Geospatial Data

By • • Knowledge, Public, Research, Teaching • , , , , , , • 3 minutes of reading

By Shahabuddin Amerudin Named Entity Recognition (NER), an integral component of Natural Language Processing (NLP), plays a pivotal role in extracting meaningful information from unstructured text. This technique involves the identification and classification of specific entities within text, ranging from names of people and organizations to temporal expressions and geographic locations. The applications of NER are wide-ranging and impactful across diverse industries. In this comprehensive article, we will delve deeper into the mechanics of NER, explore its diverse applications, and focus on a specific use case: geospatial data extraction facilitated by the EntityRecognizer model. The Mechanism Behind NER At its

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Unlocking Textual Insights: The Power and Applications of Named Entity Recognition (NER)

By • • Knowledge, Public, Research, Teaching • , , , , • 3 minutes of reading

By Shahabuddin Amerudin Named Entity Recognition (NER), often referred to as entity chunking, extraction, or identification, is a vital process in the realm of Natural Language Processing (NLP). It revolves around the identification and classification of crucial information, known as entities, within text. These entities can be single words or phrases consistently referring to the same concept. Through NER, we can automatically categorize these entities into predetermined classes, such as “Person,” “Organization,” “Time,” “Location,” and more. This computational feat yields valuable insights from extensive textual data and finds its application across a plethora of scenarios. The Mechanism Behind NER NER

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GeoAI: Merging Geospatial Data and AI for Enhanced Decision-Making

By • • Knowledge, Public, Research, Teaching • , , • 5 minutes of reading

By Shahabuddin Amerudin Geospatial Artificial Intelligence (GeoAI) is a specialized field that combines geospatial data, which includes geographic information such as location, coordinates, and spatial relationships, with artificial intelligence (AI) techniques to extract valuable insights, patterns, and predictions from spatially referenced data. In essence, GeoAI involves the application of AI algorithms and methodologies to geospatial data to solve complex problems and enhance decision-making in various domains. Key Components of GeoAI Applications of GeoAI Tools and Software Platforms for GeoAI There are several tools and software platforms available for working with GeoAI. These tools offer functionalities for processing, analyzing, visualizing, and

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GeoAI: Unveiling Patterns and Shaping Futures at the Nexus of Geography and Artificial Intelligence

By • • Knowledge, Public, Research, Teaching • , , , , • 3 minutes of reading

By Shahabuddin Amerudin Introduction In the contemporary era of technological advancements, the amalgamation of artificial intelligence (AI) with geography has ushered in a revolutionary field known as GeoAI. This interdisciplinary domain leverages the prowess of AI to decode intricate patterns concealed within geospatial data, enabling us to predict, analyze, and respond to a spectrum of events and phenomena. From predicting ecological shifts to deciphering human mobility trends, GeoAI stands as a beacon of innovation that reshapes our perception of the world. In this article, we delve deeper into the essence of GeoAI and its multifaceted applications, bringing to light its

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Exploring the Transformative Applications of Artificial Intelligence and Machine Learning in Geospatial Technology

By • • Knowledge, Project, Public, Research, Teaching • , , , , , • 4 minutes of reading

By Shahabuddin Amerudin Abstract Geospatial technology has emerged as a pivotal discipline with far-reaching implications in numerous fields, including environmental science, geography, urban planning, and agriculture. The fusion of Artificial Intelligence (AI) and Machine Learning (ML) with geospatial analysis has ushered in an era of unprecedented advancements, elevating the capabilities of geospatial technology to new heights. This comprehensive academic article delves into the multifaceted applications of AI and ML in geospatial technology, elucidating their roles in land cover mapping, flood prediction and monitoring, precision agriculture, and traffic management. By understanding these innovative applications, readers can contribute meaningfully to the evolution

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Emerging Trends in GIS Software Systems: The Impact of Artificial Intelligence on Environmental Conservation and Resource Management

By • • Knowledge, Project, Public, Research, Teaching • , , , , , , , , • 5 minutes of reading

By Shahabuddin Amerudin Abstract Geographic Information Systems (GIS) play a vital role in environmental conservation and natural resource management. In recent years, the integration of Artificial Intelligence (AI) into GIS software has led to revolutionary advancements, enhancing the capabilities and intelligence of GIS applications. This article explores the emerging trends in GIS software systems that leverage AI technologies, focusing on various aspects such as interaction methods, data visualization, predictive modelling, spatial analysis, real-time decision-making, autonomous data collection, data fusion, precision agriculture, and environmental risk assessment. These trends are transforming the way government agencies and organizations address complex environmental challenges, promoting

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