Moving dunes on the Google Earth

Several methods exist for surveying the dunes and estimate their migration rate. Among methods suitable for the macroscopic scale, the use of the satellite images available on Google Earth is a convenient resource, in particular because of its time series. Some examples of the use of this feature of Google Earth are here proposed.Comment: Keywords: Dunes, Dune Migration, Satellite Imagery, Google Earth, Image Processin

Google's Cloud Vision API Is Not Robust To Noise

Google has recently introduced the Cloud Vision API for image analysis. According to the demonstration website, the API "quickly classifies images into thousands of categories, detects individual objects and faces within images, and finds and reads printed words contained within images." It can be also used to "detect different types of inappropriate content from adult to violent content." In this paper, we evaluate the robustness of Google Cloud Vision API to input perturbation. In particular, we show that by adding sufficient noise to the image, the API generates completely different outputs for the noisy image, while a human observer would perceive its original content. We show that the attack is consistently successful, by performing extensive experiments on different image types, including natural images, images containing faces and images with texts. For instance, using images from ImageNet dataset, we found that adding an average of 14.25% impulse noise is enough to deceive the API. Our findings indicate the vulnerability of the API in adversarial environments. For example, an adversary can bypass an image filtering system by adding noise to inappropriate images. We then show that when a noise filter is applied on input images, the API generates mostly the same outputs for restored images as for original images. This observation suggests that cloud vision API can readily benefit from noise filtering, without the need for updating image analysis algorithms

3D MODELING of A COMPLEX BUILDING: From MULTI-VIEW IMAGE FUSION to GOOGLE EARTH PUBLICATION

This paper presents a pipeline that aims at illustrating the procedure to realize a 3D model of a complex building integrating the UAV and terrestrial images and modifying the 3D model in order to publish to Google Earth in an interactive modality so as to provide better available models for visualization and use. The main steps of the procedure are the optimization of the UAV flight, the integration of the different UAV and ground floor images and the optimization of the model to be published to GE. The case study has been identified in a building, The Eremo di Santa Rosalia Convent in Sicily which hash more staggered elevations and located in the hills of the hinterland and of which, the online platform only indicate the position on Google Maps (GM) and Google Earth (GE) with a photo from above and a non-urban road whose GM path is not corresponding with the GE photo. The process highlights the integration of the models and showcases a workflow for the publication of the combined 3D model to the GE platform

Transferring Google Earth observations to GIS-software : example from gully erosion study

High-resolution images available on Google Earth are increasingly being consulted in geographic studies. However, most studies limit themselves to visualizations or on-screen measurements. Google Earth allows users to create points, lines, and polygons on-screen, which can be saved as Keyhole Markup Language (KML) files. Here, the use of R statistics freeware is proposed to easily convert these files to the shapefile format [or .shp file format'], which can be loaded into Geographic Information System (GIS) software (ESRI ArcGIS 9 in our example). The geospatial data integration in GIS strongly increases the analysis possibilities

Enhancing the Google imagery using a wavelet filter

In some previous papers we proposed the use of free software for a processing of the Google satellite imagery. Here we discuss the use of a wavelet filter for the same purposes. This filter is a tool included in a freely downloadable software (Iris), well-known for the processing of astronomical images. Combining the image obtained after applying the wavelet filter, with an image created with Gimp and AstroFracTool, the visibility of the landforms, as obtained from Google Maps, is strongly increased. Among several possible examples, we proposed a crater, a paleochannel and the Great Bend of the Nile.Comment: Keywords: Satellite maps, Landforms, Image processing, Wavelet