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Figure 3: 3D modelling. for a Better Quality of Life. Partners include the University of Malta, MEPA, MRA, NSO and the Environmental Health Directorate (MEPA, 2009).

Figure 3 3D modelling. for a Better Quality of Life. Partners include the University of Malta, MEPA, MRA, NSO and the Environmental Health Directorate (MEPA, 2009).

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Figure 2: Gozo’s LIDAR scan. The process was initiated in 2006, which activity resulted in the creation of fundamental datasets that also brings Maltese terrestrial and bathymetric baseline information to the public domain. These activities have been carried out as part of a €4.6 million project, entitled Developing National Environmental Monitoring Infrastructure and Capacity, which also involves monitoring of air, Capacity, which also involves monitoring of air,
Figure 2: Gozo’s LIDAR scan. The process was initiated in 2006, which activity resulted in the creation of fundamental datasets that also brings Maltese terrestrial and bathymetric baseline information to the public domain. These activities have been carried out as part of a €4.6 million project, entitled Developing National Environmental Monitoring Infrastructure and Capacity, which also involves monitoring of air, Capacity, which also involves monitoring of air,
Figure 1: The grey area depicts the bathymetric sidescan zone, the black depicts the bathymetric LIDAR zone and the white depicts the terrestrial LIDAR zone.
Figure 1: The grey area depicts the bathymetric sidescan zone, the black depicts the bathymetric LIDAR zone and the white depicts the terrestrial LIDAR zone.
Figure 4: Oblique Imagery Output. The project ensures that new sets of baseline data layers are created from which to launch Malta’s new data capturing exercises across the different themes. Terrestrial and bathymetric data are available at higher resolutions suitable for environmental modeling and offence monitoring. As the non availability of high quality 3D spatial data hinders comprehensive land use planning and environmental monitoring, the project delivers a series of scans using different technologies in order to provide a comprehensive seamless dataset. The outputs includes high resolution 3D terrestrial data coverage for the Maltese Islands (Figure 1) using a combination of Light Detection and Ranging (LIDAR) data (Figure 2), 3D spatial models (Figure 3), oblique aerial imagery (Figure
Figure 4: Oblique Imagery Output. The project ensures that new sets of baseline data layers are created from which to launch Malta’s new data capturing exercises across the different themes. Terrestrial and bathymetric data are available at higher resolutions suitable for environmental modeling and offence monitoring. As the non availability of high quality 3D spatial data hinders comprehensive land use planning and environmental monitoring, the project delivers a series of scans using different technologies in order to provide a comprehensive seamless dataset. The outputs includes high resolution 3D terrestrial data coverage for the Maltese Islands (Figure 1) using a combination of Light Detection and Ranging (LIDAR) data (Figure 2), 3D spatial models (Figure 3), oblique aerial imagery (Figure
Figure 5: Permits.
Figure 5: Permits.
Figure 6: Green Crime. to offences committed to physical environment (Formosa, Magri, Neuschmid and Schrenk, 2011). Within such scenarios, the combination of the LIDAR scans, the reported offences and the insitu fieldwork enables the identification of infringements within sub-meter accuracy. The integration of development permitting data (Figure 5) and infringement reports (Figure 6) together with the 3D data incoming from the terrestrial and bathymetric LIDAR data would help to identify the range of offence committed, such as height over- extension, sprawl extent, pollution dispersal, noise concentrations and dumping volume. The process, though complex achieves the aim of identifying irregularities and ensures that the environment is safeguarded for the future. Updated data runs will ensure that the data is updated to allow temporal analysis where the differences between one state and the next are automatically identified and action is taken is shorter time spans.
Figure 6: Green Crime. to offences committed to physical environment (Formosa, Magri, Neuschmid and Schrenk, 2011). Within such scenarios, the combination of the LIDAR scans, the reported offences and the insitu fieldwork enables the identification of infringements within sub-meter accuracy. The integration of development permitting data (Figure 5) and infringement reports (Figure 6) together with the 3D data incoming from the terrestrial and bathymetric LIDAR data would help to identify the range of offence committed, such as height over- extension, sprawl extent, pollution dispersal, noise concentrations and dumping volume. The process, though complex achieves the aim of identifying irregularities and ensures that the environment is safeguarded for the future. Updated data runs will ensure that the data is updated to allow temporal analysis where the differences between one state and the next are automatically identified and action is taken is shorter time spans.
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