{"success":true,"data":{"query":"Spatial Computing","limit":10,"count":9,"sources":["wiki_real_estate.hat","wiki_artificial_intelligence.hat","spatial_computing.tah"],"synced":[],"results":[{"source":"wiki_real_estate.hat","text":"Management\nCadastre management has been used by the software industry since at least 2005. It mainly refers to the use of technology for management of cadastre and land information in geographic information systems, spatial data infrastructures and software architecture, rather than to general management issues of cadastral and other land information agencies.\n\nCadastres in different jurisdictions\nUnited Kingdom\nIn Scotland there is a Cadastral Map: Land Registration etc (Scotland) Act 2012. In 1836, Colonel Robert Dawson of the Royal Engineers proposed that a cadastre be implemented in light of his experiences on secondment to the Tithe Commission.\nIn England, Wales and Northern Ireland there is a system of land registration with similar functions, but the word \"cadastre\" is not used.","score":36.327294707324214,"links":[]},{"source":"wiki_artificial_intelligence.hat","text":"Perception\nMachine perception is the ability to use input from sensors (such as cameras, microphones, wireless signals, active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world. Computer vision is the ability to analyze visual input.\nThe field includes speech recognition, image classification, facial recognition, object recognition, object tracking, and robotic perception.\n\nSocial intelligence\nAffective computing is a field that comprises systems that recognize, interpret, process, or simulate human feeling, emotion, and mood. For example, some virtual assistants are programmed to speak conversationally or even to banter humorously; it makes them appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate human–computer interaction.\nHowever, this tends to give naïve users an unrealistic conception of the intelligence of existing computer agents. Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal sentiment analysis, wherein AI classifies the effects displayed by a videotaped subject.","score":31.583209025129094,"links":[]},{"source":"wiki_artificial_intelligence.hat","text":"History\nThe study of mechanical or \"formal\" reasoning began with philosophers and mathematicians in antiquity. The study of logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as \"0\" and \"1\", could simulate any conceivable form of mathematical reasoning. This, along with concurrent discoveries in cybernetics, information theory and neurobiology, led researchers to consider the possibility of building an \"electronic brain\". They developed several areas of research that would become part of AI, such as McCulloch and Pitts design for \"artificial neurons\" in 1943, and Turing's influential 1950 paper 'Computing Machinery and Intelligence', which introduced the Turing test and showed that \"machine intelligence\" was plausible.\nThe field of AI research was founded at a workshop at Dartmouth College in 1956. The first AI program, Logic Theorist, was presented at the workshop, created by future Turing Award winner Allen Newell and future Nobel Laureate Herbert A. Simon, in collaboration with J. C. Shaw. Many of the workshop attendees became the leaders of AI research in the 1960s. They and their students produced programs that the press described as \"astonishing\": computers were learning checkers strategies, solving word problems in algebra, proving logical theorems and speaking English. Artificial intelligence laboratories were set up at a number of British and U.S. universities in the latter 1950s and early 1960s.\nResearchers in the 1960s and the 1970s were convinced that their methods would eventually succeed in creating a machine with general intelligence and considered this the goal of their field. In 1965 Herbert Simon predicted, \"machines will be capable, within twenty years, of doing any work a man can do\". In 1967 Marvin Minsky agreed, writing that \"within a generation ... the problem of creating 'artificial intelligence' will substantially be solved\". They had, however, underestimated the difficulty of the problem. In 1974, both the U.S. and British governments cut off exploratory research in response to the criticism of Sir James Lighthill and ongoing pressure from the U.S. Congress to fund more productive projects. Minsky and Papert's book Perceptrons was understood as proving that artificial neural networks would never be useful for solving real-world tasks, thus discrediting the approach altogether. The \"AI winter\", a period when obtaining funding for AI projects was difficult, followed.\nIn the early 1980s, AI research was revived by the commercial success of expert systems, a form of AI program that simulated the knowledge and analytical skills of human experts. By 1985, the market for AI had reached over a billion dollars. At the same time, Japan's fifth generation computer project inspired the U.S. and British governments to restore funding for academic research. However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting winter began.\nUp to this point, most of AI's funding had gone to projects that used high-level symbols to represent mental objects like plans, goals, beliefs, and known facts. In the 1980s, some researchers began to doubt that this approach would be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition, and began to look into \"sub-symbolic\" approaches. Rodney Brooks rejected \"representation\" in general and focussed directly on engineering machines that move and survive. Judea Pearl, Lotfi Zadeh, and others developed methods that handled incomplete and uncertain information by making reasonable guesses rather than precise logic. But the most important development was the revival of \"connectionism\", including neural network research, by Geoffrey Hinton and others. In 1990, Yann LeCun successfully showed that convolutional neural networks can recognize handwritten digits, the first of many successful applications of neural networks.\nAI gradually restored its reputation in the late 1990s and early 21st century by exploiting formal mathematical methods and by finding specific solutions to specific problems. This \"narrow\" and \"formal\" focus allowed researchers to produce verifiable results and collaborate with other fields (such as statistics, economics and mathematics). By 2000, solutions developed by AI researchers were being widely used, although in the 1990s they were rarely described as \"artificial intelligence\" (a tendency known as the AI effect).\nHowever, several academic researchers became concerned that AI was no longer pursuing its original goal of creating versatile, fully intelligent machines. Beginning around 2002, they founded the subfield of artificial general intelligence (or \"AGI\"), which had several well-funded institutions by the 2010s.\nDeep learning began to dominate industry benchmarks in 2012 and was adopted throughout the field.\nFor many specific tasks, other methods were abandoned.\nDeep learning's success was based on both hardware improvements (faster computers, graphics processing units, cloud computing) and access to large amounts of data (including curated datasets, such as ImageNet). Deep learning's success led to an enormous increase in interest and funding in AI. The amount of machine learning research (measured by total publications) increased by 50% in the years 2015–2019.","score":31.583209025129094,"links":[]},{"source":"wiki_artificial_intelligence.hat","text":"Neat vs. scruffy\n\"Neats\" hope that intelligent behavior is described using simple, elegant principles (such as logic, optimization, or neural networks). \"Scruffies\" expect that it necessarily requires solving a large number of unrelated problems. Neats defend their programs with theoretical rigor, scruffies rely mainly on incremental testing to see if they work. This issue was actively discussed in the 1970s and 1980s, but eventually was seen as irrelevant. Modern AI has elements of both.\n\nSoft vs. hard computing\nFinding a provably correct or optimal solution is intractable for many important problems. Soft computing is a set of techniques, including genetic algorithms, fuzzy logic and neural networks, that are tolerant of imprecision, uncertainty, partial truth and approximation. Soft computing was introduced in the late 1980s and most successful AI programs in the 21st century are examples of soft computing with neural networks.","score":31.583209025129094,"links":[]},{"source":"wiki_real_estate.hat","text":"See also\nReferences\nExternal links\nReal Estate Institute of Australia (REIA) official website\n\n--- NEXT ARTICLE ---\n\nARTICLE: Cadastral surveying\nCadastral surveying is the sub-field of cadastre and surveying that specialises in the establishment and re-establishment of real property boundaries. It involves the physical delineation of property boundaries and determination of dimensions, areas and certain rights associated with properties. This is regardless of whether they are on land, water or defined by natural or artificial features. It is an important component of the legal creation of properties. A cadastral surveyor must apply both the spatial-measurement principles of general surveying and legal principles such as respect of neighboring titles.","score":27.551529804882808,"links":[]},{"source":"wiki_real_estate.hat","text":"Extensions\nExtensions of the conventional cadastre concept include the 3D cadastre, considering the vertical domain; and the multipurpose cadastre, considering non-parcel data.\nAccording to the UN Economic Commission for Europe, a \"Marine Cadastre describes the location and spatial extent of rights, restrictions and responsibilities in the marine environment\". Marine cadastres apply the same governance principles to the water. They help further conservation and sustainability efforts. This is especially a concern in Europe's large aquatic market. In Australia, they are used by many parties to plan around legal, technical, and institutional considerations. A related concept is that of marine spatial data infrastructures.\n\nSee also\nReferences\nThis article incorporates text from a publication now in the public domain: Chisholm, Hugh, ed. (1911). \"Cadastre\". Encyclopædia Britannica (11th ed.). Cambridge University Press.","score":18.775764902441402,"links":[]},{"source":"spatial_computing.tah","text":"Spatial Math: Avoid Euler angles to prevent Gimbal Lock. Use THREE.Quaternion for all rotations. Interpolation: Use .slerp() for smooth orientation changes. Matrix Ops: Use matrix.decompose(pos, quat, scale) to extract transforms. Culling: Manual frustum culling via camera.frustum.containsPoint() for large-scale procedural data.","score":15,"links":[]},{"source":"spatial_computing.tah","text":"Spatial Indexing: BVH (Bounding Volume Hierarchy) is standard for complex raycasting/picking (use three-mesh-bvh). Octrees are best for sparse 3D point-clouds. Spatial Hashing: O(1) constant-time neighbor lookups for uniform dynamic objects (particles/boids).\n\n[SWARM_LINKS] 2b2bb4b414ee -> UNRESOLVED","score":15,"links":[]},{"source":"spatial_computing.tah","text":"Hybrid Spatial Pattern: Integrate Rotoscope SAM 2 masks as dynamic textures or sprites. Use Raycasting (BVH optimized) to project mouse/touch coordinates onto the 3D 'TacticalCloth' mesh. Apply physics forces programmatically using the mesh vertex data extracted via Matrix decomposition.\n\n[SWARM_LINKS] 7b0e4df6bcdd -> UNRESOLVED, e76e5f73b131 -> UNRESOLVED","score":15,"links":[]}]},"metadata":{},"timestamp":"2026-07-08T22:44:44.348Z"}