Surveying technologies introduction
The schema provided in image below is the classification approach chosen in this project, although there are several others that could be chosen. For instance, a more general approach would be to classify the technologies into global or local surveying. In addition, a deeper class would depend on the type of measure (static and/or dynamic).
These technologies rely on sensors for data acquisition and processing to obtain raw data that support digitalization through data analysis to obtain meaningful information. Sensing technologies are based on transducers that produce an output signal to study and sample a physical phenomenon with the aim of attaining information about the condition of the physical property of interest. To this end, a distinction can be made between: (i) primary sensors, as transducers from a specific physical domain to other physical domain and (ii) sensors that provide a signal in the electrical domain. In certain cases, the physical magnitude of interest cannot be measured directly and it is necessary to use sensors that measure related physical phenomena. This type of indirect measurement is also known as proxy sensors. Low-power electrical signals may need a conditioning step to amplify the signal and filter the noise in the measurements. Conditioned signal can be stored or transmitted to an intelligent device for processing.
Sensors can also be classified depending on their need of energy sources to perform the sampling and can be differentiated as: (i) passive sensors, if they only use an external source of energy (such as a digital camera); and (ii) active sensors, which are those that need an internal source of energy to perform the sampling of the interest phenomenon (e.g., RADAR or SONAR sensors). Both active and passive sensors can be placed in several platforms that support the sampling process in a variety of conditions and locations. These include terrestrial platforms that consist of on-site fixed systems for monitoring and mobile systems for surveying, and, with the lowering access costs, aerial platforms based on unmanned aerial vehicles (UAVs) and satellites.
Regardless their classification, the signals that come from a sensor need to be digitized to obtain the so-called raw digital data. This process of Analog-to-Digital conversion is always subjected to quantization error and depends on the digital resolution of the sample but, as a result, a raw digital data can be obtained. This raw data, after a processing step, results in the digital data that can be considered as basis for digitalization.