Road Extraction for Emergencies from Satellite Imagery

Abstract

After earthquakes, international and national organizations must overcome many challenges in rescue operations. Among these, the knowledge of the territory and of the roads is fundamental for international aid. The maps that volunteers make are a valuable asset, showing the roads in the area affected by the seismic events, a knowledge which is necessary to bring rescue. This was very helpful during many earthquakes as in Haiti (on 2010-01-12) and in Nepal (on 2015-04-25) to support the humanitarian organizations. Many volunteers can contribute remotely to mapping little known or inaccessible regions with crowdsourcing actions, by tracing maps from satellite imagery or aerial photographs even if staying far from the affected site. This research, still in progress, aims at experiencing quickly obtaining roads through the so-called Object Based Image Analysis (OBIA), by extracting it from satellite data, semi-automatically or automatically, with a segmentation that starts from concepts of Mathematical Morphology. We compared it with a classification in ENVI and, using an algorithm in GIS, we verified the goodness of the method. The good results obtained encourage further research on fast techniques for map integration for humanitarian emergencies moreover the results were implemented on open street map.

SatelliteRF: Accelerating 3D Reconstruction in Multi-View Satellite Images with Efficient Neural Radiance Fields

“…As high-resolution satellite images play an increasingly important role in geospatial cognition, using these images to perform large-scale 3D models of the Earth's surface has become a research hotspot in the fields of remote sensing and photogrammetry [1][2][3][4]. Compared with traditional stereo or tri-stereo photogrammetry methods and the use of active sensor technologies such as lidar, 3D reconstruction methods based on multi-view satellite images are widely used in urban mapping [5,6], environmental monitoring [7], navigation [8], and emergency rescue [9,10] due to their advantages in terms of easily accessible data, high collection efficiency, and low cost. However, 3D reconstructions based on multi-view satellite images still face many challenges.…”

Experimenting Agriculture 4.0 with Sensors: A Data Fusion Approach between Remote Sensing, UAVs and Self-Driving Tractors

“…The MultiSpectral Instrument (MSI) of Sentinel 2 covers large areas, and many satellite programs (i.e., Landsat, Sentinel-1 and Sentinel-2) now freely supply datasets, which promotes the exploitation of satellite imagery for many applications, including agricultural applications, as multi-sensor and multiresolution data fusion [ 22 , 23 , 24 , 25 , 26 ]. The Sentinel-2 satellite, which was developed by the European Space Agency (ESA), has a resolution of one decametre, a revisitation time of six days, and an efficient resolution for analysing crop variability and conditions.…”