Aerial photographyAerial photography (or airborne imagery) is the taking of photographs from an aircraft or other airborne platforms. When taking motion pictures, it is also known as aerial videography. Platforms for aerial photography include fixed-wing aircraft, helicopters, unmanned aerial vehicles (UAVs or "drones"), balloons, blimps and dirigibles, rockets, pigeons, kites, or using action cameras while skydiving or wingsuiting. Handheld cameras may be manually operated by the photographer, while mounted cameras are usually remotely operated or triggered automatically.
Satellite imagerySatellite images (also Earth observation imagery, spaceborne photography, or simply satellite photo) are s of Earth collected by imaging satellites operated by governments and businesses around the world. Satellite imaging companies sell images by licensing them to governments and businesses such as Apple Maps and Google Maps. The first images from space were taken on sub-orbital flights. The U.S-launched V-2 flight on October 24, 1946, took one image every 1.5 seconds.
Machine learningMachine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.
Unmanned aerial vehicleAn unmanned aerial vehicle (UAV), commonly known as a drone, is an aircraft without any human pilot, crew, or passengers on board. UAVs were originally developed through the twentieth century for military missions too "dull, dirty or dangerous" for humans, and by the twenty-first, they had become essential assets to most militaries. As control technologies improved and costs fell, their use expanded to many non-military applications.
Aerial photographic and satellite image interpretationAerial photographic and satellite image interpretation, or just image interpretation when in context, is the act of examining s, particularly airborne and , to identify objects and judging their significance. This is commonly used in military aerial reconnaissance, using photographs taken from reconnaissance aircraft and reconnaissance satellites. The principles of image interpretation have been developed empirically for more than 150 years.
Aerial surveyAerial survey is a method of collecting geomatics or other ry by using airplanes, helicopters, UAVs, balloons or other aerial methods. Typical types of data collected include aerial photography, Lidar, remote sensing (using various visible and invisible bands of the electromagnetic spectrum, such as infrared, gamma, or ultraviolet) and also geophysical data (such as aeromagnetic surveys and gravity. It can also refer to the chart or map made by analysing a region from the air.
Supervised learningSupervised learning (SL) is a paradigm in machine learning where input objects (for example, a vector of predictor variables) and a desired output value (also known as human-labeled supervisory signal) train a model. The training data is processed, building a function that maps new data on expected output values. An optimal scenario will allow for the algorithm to correctly determine output values for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way (see inductive bias).
Online machine learningIn computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once. Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset, requiring the need of out-of-core algorithms.
Aerial reconnaissanceAerial reconnaissance is reconnaissance for a military or strategic purpose that is conducted using reconnaissance aircraft. The role of reconnaissance can fulfil a variety of requirements including artillery spotting, the collection of , and the observation of enemy maneuvers. Aerial photography#History and Imagery intelligence#History Espionage balloon After the French Revolution, the new rulers became interested in using the balloon to observe enemy manoeuvres and appointed scientist Charles Coutelle to conduct studies using the balloon L'Entreprenant, the first military reconnaissance aircraft.
Image analysisImage analysis or imagery analysis is the extraction of meaningful information from s; mainly from s by means of techniques. Image analysis tasks can be as simple as reading bar coded tags or as sophisticated as identifying a person from their face. Computers are indispensable for the analysis of large amounts of data, for tasks that require complex computation, or for the extraction of quantitative information.
PhotogrammetryPhotogrammetry is the science and technology of obtaining reliable information about physical objects and the environment through the process of recording, measuring and interpreting photographic images and patterns of electromagnetic radiant imagery and other phenomena. The term photogrammetry was coined by the Prussian architect Albrecht Meydenbauer, which appeared in his 1867 article "Die Photometrographie." There are many variants of photogrammetry. One example is the extraction of three-dimensional measurements from two-dimensional data (i.
Medical image computingMedical image computing (MIC) is an interdisciplinary field at the intersection of computer science, information engineering, electrical engineering, physics, mathematics and medicine. This field develops computational and mathematical methods for solving problems pertaining to medical images and their use for biomedical research and clinical care. The main goal of MIC is to extract clinically relevant information or knowledge from medical images.
Disaster responseDisaster response refers to the actions taken directly before, during or in the immediate aftermath of a disaster. The objective is to save lives, ensure health and safety and to meet the subsistence needs of the people affected. This includes warning/evacuation, search and rescue, providing immediate assistance, assessing damage, continuing assistance and the immediate restoration or construction of infrastructure (i.e. provisional storm drains or diversion dams).
Unsupervised learningUnsupervised learning, is paradigm in machine learning where, in contrast to supervised learning and semi-supervised learning, algorithms learn patterns exclusively from unlabeled data. Neural network tasks are often categorized as discriminative (recognition) or generative (imagination). Often but not always, discriminative tasks use supervised methods and generative tasks use unsupervised (see Venn diagram); however, the separation is very hazy. For example, object recognition favors supervised learning but unsupervised learning can also cluster objects into groups.
Wildlife conservationWildlife conservation refers to the practice of protecting wild species and their habitats in order to maintain healthy wildlife species or populations and to restore, protect or enhance natural ecosystems. Major threats to wildlife include habitat destruction, degradation, fragmentation, overexploitation, poaching, pollution, climate change, and the illegal wildlife trade. The IUCN estimates that 42,100 species of the ones assessed are at risk for extinction.
Feature learningIn machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.
Unmanned combat aerial vehicleAn unmanned combat aerial vehicle (UCAV), also known as a combat drone, colloquially shortened as drone or battlefield UAV, is an unmanned aerial vehicle (UAV) that is used for intelligence, surveillance, target acquisition, and reconnaissance and carries aircraft ordnance such as missiles, ATGMs, and/or bombs in hardpoints for drone strikes. These drones are usually under real-time human control, with varying levels of autonomy. Unlike unmanned surveillance and reconnaissance aerial vehicles, UCAVs are used for both drone strikes and battlefield intelligence.
Aerial warfareAerial warfare is the use of military aircraft and other flying machines in warfare. Aerial warfare includes bombers attacking enemy installations or a concentration of enemy troops or strategic targets; fighter aircraft battling for control of airspace; attack aircraft engaging in close air support against ground targets; naval aviation flying against sea and nearby land targets; gliders, helicopters and other aircraft to carry airborne forces such as paratroopers; aerial refueling tankers to extend operation time or range; and military transport aircraft to move cargo and personnel.
Artificial neural networkArtificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.
DisasterA disaster is a serious problem occurring over a period of time that causes widespread human, material, economic or environmental loss which exceeds the ability of the affected community or society to cope using its own resources. Disasters are routinely divided into either "natural disasters" caused by natural hazards or "human-instigated disasters" caused from anthropogenic hazards. However, in modern times, the divide between natural, human-made and human-accelerated disasters is difficult to draw.
