Artificial Intelligence in surveying

What is intelligence?

The ability of problem solving demonstrates intelligence. Consider a mouse trying to search/reach the piece of cheese placed at right top corner of the image the mouse can find more than one solutions to this problem. We can say that the mouse is intelligent enough to find a solution to the problem. Hence the ability of problem solving demonstrates intelligence. Intelligence is the computational part of the ability to achieve goals in the world, varying kinds and degrees of intelligence occur in people, many animals and some machines.

Artificial Intelligence, or AI for short, is a combination of computer science, physiology, and philosophy. AI is a broad topic, consisting of different fields, from machine vision to expert systems. The element that the fields of AI have in common is the creation of machines that can "think". One of the most challenging approaches facing experts is building systems that mimic the behaviour of the human brain, made up of billions of neurons, and arguably the most complex matter in the universe.

 Artificial Intelligence

These benefits can also be seen in several new technologies that are being driven by Artificial Intelligence (AI). This includes Building Information Modelling (BIM), a system which involves creating a comprehensive digital description that will be worked on collaboratively, throughout each stage of the project. These precise 3D models contain a wealth of data relating to every physical and functional element of the build. Concerning AI, the ability to incorporate machine learning with qualities of human intelligence has already began to infiltrate these systems. For instance, the analysis of large quantities of data has several practical applications for quality control assessments. More specifically, by utilising theoretical techniques, AI can successfully optimise the process and effectively account for any logistically issues well in advance. Resulting in significant savings being made once the project finally reaches the building phase.

When it comes to surveying, the combination of AI and BIM has several major implications for the profession. By automating many of their core responsibilities, surveyors will find that BIM enables them to more effectively conduct their day-to-day work. For example, the ability to access a shared 3D visualization of the site assists in their analysis and control over the process.

Problems Of Artificial Intelligence

A. Deduction, reasoning, problem solving Early AI researchers developed algorithms that imitated the step-by-step reasoning that human beings use when they solve puzzles, play board games or make logical deductions. By the late 80s and 90s, AI research had also developed highly successful methods for dealing with uncertain or incomplete information, employing concepts from probability and economics. For difficult problems, most of these algorithms can require enormous computational resources — most experience a "combinatorial explosion": the amount of memory or computer time required becomes astronomical when the problem goes beyond a certain size. The search for more efficient problem solving algorithms is a high priority for AI research. Human beings solve most of their problems using fast, intuitive judgments rather than the conscious, step-by-step deduction that early AI research was able to model. AI has made some progress at imitating this kind of "sub-symbolic" problem solving: embodied approaches emphasize the importance of sensor motor skills to higher reasoning; neural net research attempts to simulate the structures inside human and animal brains that gives rise to this skill.

B. Knowledge representation

Knowledge representation and knowledge engineering are central to AI research. Many of the problems machines are expected to solve will require extensive knowledge about the world. Among the things that AI needs to represent are: objects, properties, categories and relations between objects; situations, events, states and time; causes and effects; knowledge about knowledge (what we know about what other people know); and many other, less well researched domains. A complete representation of "what exists" is an ontology (borrowing a word from traditional philosophy), of which the most general are called upper ontology.

C. Planning

Intelligent agents must be able to set goals and achieve them. They need a way to visualize the future (they must have a representation of the state of the world and be able to make predictions about how their actions will change it) and be able to make choices that maximize the utility (or "value") of the available choices. In some planning problems, the agent can assume that it is the only thing acting on the world and it can be certain what the consequences of its actions may be. However, if this is not true, it must periodically check if the world matches its predictions and it must change its plan as this becomes necessary, requiring the agent to reason under uncertainty.

D. Learning Machine

 learning has been central to AI research from the beginning. Unsupervised learning is the ability to find patterns in a stream of input. Supervised learning includes both classification (be able to determine what category something belongs in, after seeing a number of examples of things from several categories) and regression (given a set of numerical input/output examples, discover a continuous function that would generate the outputs from the inputs). In reinforcement learning the agent is rewarded for good responses and punished for bad ones. These can be analyzed in terms of decision theory, using concepts like utility. The mathematical analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory.

 E. Natural language processing

Natural language processing gives machines the ability to read and understand the languages that the human beings speak. Many researchers hope that a sufficiently powerful natural language processing system would be able to acquire knowledge on its own, by reading the existing text available over the internet. Some straightforward applications of natural language processing include information retrieval (or text mining) and machine translation

. F. Motion and Manipulation

 ASIMO uses sensors and intelligent algorithms to avoid obstacles and navigate stairs. The field of robotics is closely related to AI. Intelligence is required for robots to be able to handle such tasks as object manipulation and navigation, with sub-problems of localization (knowing where you are), mapping (learning what is around you) and motion planning (figuring out how to get there).

G. Perception Machine perception is the ability to use input from sensors (such as cameras, microphones, sonar and others more exotic) to deduce aspects of the world. Computer vision is the ability to analyze visual input. A few selected sub problems are speech recognition, facial recognition and object recognition. H. Creativity A sub-field of AI addresses creativity both theoretically (from a philosophical and psychological perspective)

Drones

Firstly, the surveying industry has seen a significant growth in the use of drone related technology. This is because, by making use of these unmanned aerial vehicles (UAVs), surveyors can now conduct their day-to-day tasks with relative ease and increased efficiency, as well as gather useful information for future consideration.

More specifically, the mobility and verticality of these UAVs have allowed surveyors to inspect areas that have previously been deemed unsafe for access. Not only reducing the risk of personal injury for many inspectors, but also providing a cost-effective solution to a previously time-consuming task. By making use of the device’s autonomous controls, a surveyor can program a drone to automatically take a large aerial map of a site, before returning to its original location and uploading the material to a secure sever. This increased mobility and automation not only saves time during the actual inspection, but also allows the surveyor to collect several high-resolution images of these locations for future reference. In some cases, these images can even be collated into detailed, photorealistic 3D maps of the area. Meaning that the data can be interpreted via a highly accurate visual that not only reduces the need for excessive jargon, but also the risk of potential discrepancies or inconsistencies.

The benefits of Drones in the surveying industry has become so prevalent that some firms have even begun to host Continuing Professional Development (CPD) courses on their uses. However, it should also be noted that, under the Civil Aviation Authority (CAA) a restricted or full NQE (National Qualified Entities) is now required for any type of commercial drone use.

Field Work On Steroids

When the time arrives for field work to begin, a technician is dispatched in an autonomous electric truck pre-programmed to go directly to the site. The truck is loaded with various survey-grade instruments and equipment (all GNSS equipped): vertical take-off fixed wing and multi-rotor UAVs (both with lidar, photo, hyper-spectral, and GPR sensors), an autonomous mobile ground robot (with GPR/lidar sensors), and an RFID reader for boundary location.

The technician works with the equipment through a universal tablet computer controlling both aerial and ground data collection simultaneously, depicting the progress of the work in real time. This gives the technician time to locate the boundary points with the handheld GNSS receiver/RFID reader to verify the limits of the property.

Once the autonomous work is finished, the technician processes the data on site, and software compares collection coverage versus the initial site review. When processing is complete, the technician will utilize a handheld GNSS receiver with lidar sensor to obtain remote areas not collected by the other methods.

The remaining data is compiled with autonomous data and re-analyzed for overall coverage and approved by the software for completeness. Once the computer determines everything has been collected, the technician checks the complete box and leaves the site.

Office Work And Wrap-Up

The final field data is uploaded to cloud servers as the technician leaves the site and the survey PM is notified by electronic message of the field task completion. Thomas, the digital surveying assistant, takes the lead and begins the final processing. The data is reviewed for completeness, parsed for any anomalies within the downloads, and compiled into one database for building a 3D model of the site.

Photo and lidar data are compared for accuracy, utilities are verified against existing records and easements, and building characteristics are matched against governmental records for zoning code compliance.

 Once this analysis is complete, the final drafting takes place to create the final deliverable. While the data within the model contains attributes of each entity, labels are placed interactively throughout the site to help depict the site information. This model is also suitable for use by architects and planners to utilize in their B.I.M. design programs, so the quality in the modeling output is top notch.

The final deliverable contains an overall report documenting site conditions, drainage characteristics and physical conditions of various entities. This report will also detail potential site encroachments, possible drainage issues, and zoning/parking red flags. Thomas will report back to the survey PM that all final checks have been made and deliverables made for submittal to the client, leaving only the final transmittal left to do.

Once the deliverable is received by the client, Sheldon (the B2B automated assistant) recognizes the delivery and begins the process of payment to the surveyor. With standardized surveys, automated assistant/analyzation systems, and trackable processes through blockchain, the client gets a quality product at a market rate in an acceptable timeframe and the surveyor gets paid in a reasonable period.