The world’s continuously increasing population leads to environmental challenges, among which, the urban heat island effect has been recognized as one of the leading environmental issues recently. Using traditional weather station (usually one or two within one city and placed in rural area) to monitor and model the canopy layer urban heat phenomenon does not provide enough spatial resolution. Alternatively, the Netatmo weather station, a low cost and citizen science weather sensor, is able to collect crowdsourced temperature records and has significant strength in spatial and temporal resolution in temperature measurement. Thanks to the variety of uses of the Netatmo weather station and its open API, more temperature data could be used for UHI research. However, for scientific use, the main challenge is the data quality. For one thing, the stations’ locations are set by users and are thus not accurate enough for temperature modeling in a complex city environment. For another, sensors some time generate unreliable records when exposed to solar radiance directly. These two things are actually highly interactional. Knowing the accurate location of stations could be helpful to calculate when the stations are exposed sun then filter outliers, and vice versa. However, the location information could be used to improve its accuracy is quite limited. Thus, the current work is focusing on develop an approach to determine the likely correct location of the stations. For the development of the relocation method, different spatial and sensor datasets have been used. The temperature data in the Hague in May, 2018 have been collected from Netatmo weather stations. Additionally, the AHN3 points cloud for solar simulation and BGT shapefile for creating new location have been investigated. The methodology of relocation process is divided into 6 steps: Sensor data pre-processing, Detecting higher temperature time, Generating potential location of stations, Computing sky view (dome) and solar parameter, Finding the most likely horizontal location of the station, and Assigning height value to points. These steps also have been used with another period time in the Hague for validation and one sample Netatmo sensor experiment in Delft will be conducted. The results proved the feasibility and rationality of the adopted methodology. Around 67% stations (new location) is shown more than 0.5 similarity when comparing with their solar simulation. Validation result detained by two period comparison indicates that over 70% Netatmo stations’ new location show high quality on both the horizontal and vertical dimensions after applying the process. Validation experiment is shown a real example of fluctuated air temperature and how it will be influenced by solar radiance. In the experiment, the location error is reduced from 16 meters to 4 meters, which proves that the methodology adopted by the project is helpful to improve the station’s location accuracy.

Every day, terabytes of information is generated, filling storage devices around the world. However,the human brain have limited capacities to read and understand raw data from a computer screen.That is why data specialists need to ingeniously create better ways to display, process and analyzemassive amounts of data.Our research project is not about avoiding subsidence, not even about cracks on buildings; it ispurely data analysis and interpretation. This study will help professionals understand and fightagainst building subsidence. Our task was to create, manipulate and make sense of charts like theone below (a real line graph from InSAR data), then translate them into useful information forstakeholders in the local, national and global community.The aim of the project was to understand if ground sensor technologies are comparable to othersources of information. In our analysis different strategies to analyze building subsidence wereimplemented, e.g. homogeneous subsidence, heterogeneous subsidence and for water levels,interpolation and cross correlation methods. In addition, other techniques like sensor fusing wereimplemented to compare data from different sources.As a result from all these strategies, we can say that the water level sensors placed in our researchbuilding, have a high similarity with citizens and municipality data. In contrast, InSAR data is notcomparable with the subsidence sensors placed in the building because they have differentreferences and the period of study was too short to get accurate results from satellite data. Finally,an idea for future implementation strategies was proposed. On this idea, measurements of levelscan be carried out taking as a reference the NAP level and comparing the subsidence between ahealthy-foundations building and another one with wooden-piles foundation.