Toward a Deep Learning Approach for Automatic Semantic Segmentation of 3D Lidar Point Clouds in Urban Areas

peer reviewedSemantic segmentation of Lidar data using Deep Learning (DL) is a fundamental step for a deep and rigorous understanding of large-scale urban areas. Indeed, the increasing development of Lidar technology in terms of accuracy and spatial resolution offers a best opportunity for delivering a reliable semantic segmentation in large-scale urban environments. Significant progress has been reported in this direction. However, the literature lacks a deep comparison of the existing methods and algorithms in terms of strengths and weakness. The aim of the present paper is therefore to propose an objective review about these methods by highlighting their strengths and limitations. We then propose a new approach based on the combination of Lidar data and other sources in conjunction with a Deep Learning technique whose objective is to automatically extract semantic information from airborne Lidar point clouds by enhancing both accuracy and semantic precision compared to the existing methods. We finally present the first results of our approach

The Hassan mosque at the digital era

About ten years ago, the double behest of late Driss Aboutajdine and El Mustapha Mouaddib permitted setting up research partnership on the use of image processing and cultural heritage. In 2015, despite his workload, Professor Driss Aboutajdine has put all his energy so a common complementary action could take place and occur, convening hence the numerical sciences, precisely 3D techniques, serving cultural heritage. This action went on to give birth to Athar-3D project, with the ambition to resolve questions pertaining to 3D modeling andcomputer vision having along a positive impact on cultural and architectural heritage perception. The research we carried out in this framework aims to the digitizing of Hassan Mosque, its reconstitution and the achievement of mechanism and application to heighten awareness and to better know and communicate about cultural heritage matter. To our knowledge, this is the first work of its kind and with this scientific extent on cultural and architectural heritage in Morocco. Moreover, finding an extension of academic research in the dissemination, the bringing back and mediation on this monument that stands for a symbol and emblem of Morocco’s capital, is a direct valorization of research work performed in Moroccan research laboratories. This paper presents representative results of the whole project, including its historical and arts history, especially on Rabat Hassan Mosque. We are providing, for the first time, results that make possible 3D display of what Hassan Mosque might looks like. This model with the vocation to be a scientific support and medium and to which we attempted to bring all the necessary rigor. This will serve the scientific study of the monument, the popularization and awareness raising with respect to cultural heritage matters in general and Hassan Mosque in particular.We hope, therefore, to remain faithful to one of the wishes of Professor Driss Aboutajdine, which is to ensure that scientific research directly impact the societ

The Hassan mosque at the digital era.

International audienceAbout ten years ago, the double behest of late Driss Aboutajdine and El Mustapha Mouad-dib permitted setting up research partnership on the use of image processing and cultural heritage. In 2015, despite his workload, Professor Driss Aboutajdine has put all his energy so a common complementary action could take place and occur, convening hence the numerical sciences, precisely 3D techniques, serving cultural heritage. This action went on to give birth to Athar-3D project, with the ambition to resolve questions pertaining to 3D modeling and computer vision having along a positive impact on cultural and architectural heritage perception. The research we carried out in this framework aims to the digitizing of Hassan Mosque, its reconstitution and the achievement of mechanism and application to heighten awareness and to better know and communicate about cultural heritage matter. To our knowledge, this is the first work of its kind and with this scientific extent on cultural and architectural heritage in Morocco. Moreover, finding an extension of academic research in the dissemination, the bringing back and mediation on this monument that stands for a symbol and emblem of Morocco's capital, is a direct valorization of research work performed in Moroccan research laboratories. This paper presents representative results of the whole project, including its historical and arts history, especially on Rabat Hassan Mosque. We are providing, for the first time, results that make possible 3D display of what Hassan Mosque might looks like. This model with the vocation to be a scientific support and medium and to which we attempted to bring all the necessary rigor. This will serve the scientific study of the monument, the popularization and awareness raising with respect to cultural heritage matters in general and Hassan Mosque in particular. We hope, therefore, to remain faithful to one of the wishes of Professor Driss Aboutajdine

Automatic Sub-Pixel Co-Registration of Remote Sensing Images Using Phase Correlation and Harris Detector

In this paper, we propose a new approach for sub-pixel co-registration based on Fourier phase correlation combined with the Harris detector. Due to the limitation of the standard phase correlation method to achieve only pixel-level accuracy, another approach is required to reach sub-pixel matching precision. We first applied the Harris corner detector to extract corners from both references and sensed images. Then, we identified their corresponding points using phase correlation between the image pairs. To achieve sub-pixel registration accuracy, two optimization algorithms were used. The effectiveness of the proposed method was tested with very high-resolution (VHR) remote sensing images, including Pleiades satellite images and aerial imagery. Compared with the speeded-up robust features (SURF)-based method, phase correlation with the Blackman window function produced 91% more matches with high reliability. Moreover, the results of the optimization analysis have revealed that Nelder–Mead algorithm performs better than the two-point step size gradient algorithm regarding localization accuracy and computation time. The proposed approach achieves better accuracy than 0.5 pixels and outperforms the speeded-up robust features (SURF)-based method. It can achieve sub-pixel accuracy in the presence of noise and produces large numbers of correct matching points

Building information modeling potential for an enhanced real estate valuation approach based on the hedonic method

Housing valuation is a process of determining an accurate estimate of the market price of a property. Current methods and tools are mainly based on sales prices comparison with recent transactions, which is the major method applied in the taxation and cadastre services in Morocco. However, housing properties are in general heterogenous and unique in their shapes, construction materials, orientation, location and other environmental factors. These parameters are taken into consideration by the hedonic pricing method. Many of the researches about housing valuation are based on the geographical location as the main spatial factor affecting the property value. 2D GIS (Geographic Information System) applications used in this respect are limited in terms of communicating efficiently the complexity of a 3D building structure and modeling accurately environmental factors. Such factors could only be considered through 3D modeling and building information modeling (BIM). In this paper, we will try, through a brief review, to point out the weaknesses and drawbacks of the conventional valuation methods. Then, we will demonstrate the BIM potential in real valuation as an emerging technology and process used to mainly improve the housing valuation system based on the hedonic approach. Many studies are, nowadays, widely exploring the use of BIM in the building cost estimation, but this is an embryonic area of research in real estate valuation system. Therefore, this paper examines also the first methodological guidelines for an advanced housing valuation approach by implementing a BIM prototype based on the hedonic pricing method

Mapping Wheat Dry Matter and Nitrogen Content Dynamics and Estimation of Wheat Yield Using UAV Multispectral Imagery Machine Learning and a Variety-Based Approach: Case Study of Morocco

Our work aims to monitor wheat crop using a variety-based approach by taking into consideration four different phenological stages of wheat crop development. In addition to highlighting the contribution of Red-Edge vegetation indices in mapping wheat dry matter and nitrogen content dynamics, as well as using Random Forest regressor in the estimation of wheat yield, dry matter and nitrogen uptake relying on UAV (Unmanned Aerial Vehicle) multispectral imagery. The study was conducted on an experimental platform with 12 wheat varieties located in Sidi Slimane (Morocco). Several flight missions were conducted using eBee UAV with MultiSpec4C camera according to phenological growth stages of wheat. The proposed methodology is subdivided into two approaches, the first aims to find the most suitable vegetation index for wheat’s biophysical parameters estimation and the second to establish a global model regardless of the varieties to estimate the biophysical parameters of wheat: Dry matter and nitrogen uptake. The two approaches were conducted according to six main steps: (1) UAV flight missions and in-situ data acquisition during four phenological stages of wheat development, (2) Processing of UAV multispectral images which enabled us to elaborate the vegetation indices maps (RTVI, MTVI2, NDVI, NDRE, GNDVI, GNDRE, SR-RE et SR-NIR), (3) Automatic extraction of plots by Object-based image analysis approach and creating a spatial database combining the spectral information and wheat’s biophysical parameters, (4) Monitoring wheat growth by generating dry biomass and wheat’s nitrogen uptake model using exponential, polynomial and linear regression for each variety this step resumes the varietal approach, (5) Engendering a global model employing both linear regression and Random Forest technique, (6) Wheat yield estimation. The proposed method has allowed to predict from 1 up to 21% difference between actual and estimated yield when using both RTVI index and Random Forest technique as well as mapping wheat’s dry biomass and nitrogen uptake along with the nitrogen nutrition index (NNI) and therefore facilitate a careful monitoring of the health and the growth of wheat crop. Nevertheless, some wheat varieties have shown a significant difference in yield between 2.6 and 3.3 t/ha

Les spécificaitons techniques des variables 3D pour la modélisation de l'évaluation immobilière basée sur l'intégration des BIM et CIM

peer reviewedAbstract:The growing rate of urbanization and vertical urban development has aroused the sig-nificance of geo-related variables for property units disposed vertically within the same building.Among these, 3D indoor physical and outdoor environmental variables are impacting the propertyvalue for each building unit. However, in the literature, the identified 3D variables, by using hedonicpricing models (HPM) for property valuation, are mainly restricted to 3D visualization. Their usein 3D simulation for an accurate evaluation of the property value is still limited. Furthermore,their value is often defined for a specific valuation purpose (e.g., taxation). This paper aims toinvestigate 3D variables with a significant impact on property value, to combine them with 3Dtechnical requirements and to be integrated in a future valuation model. Moreover, their 3D spatialand non-spatial elements are analyzed to identify which variables can be provided from 3D citymodels and building scale elements. To accomplish this, the potential of 3D building informationmodeling (BIM) and city information modeling (CIM) in property valuation is examined. Fromindoors; BIM/IFC (Industry Foundation Classes) models are the main data sources for structuraland living quality variables. While from outdoors, environmental variables and the surroundingbuilding’s information are provided from 3D city models (CityGML