The results show that the recommended configuration (Height Shift Range = 0.2 Width Shift Range = 0.2 Zoom Range =0.2) reached an accuracy of 95.6%\documentclass. Experiments were carried out with three architectures of Deep Learning from the literature using the Keras library. In order to do that, Logistic Regression models were used to analyze the performance of Convolutional Neural Networks trained from 128 combinations of transformations in the images. This paper proposes a rigorous methodology for tuning of Data Augmentation hyperparameters in Deep Learning to building construction image classification, especially to vegetation recognition in facades and roofs structure analysis. One challenge of this application is Convolutional Neural Networks adoption in a small datasets. ĭeep Learning methods have important applications in the building construction image classification field.
Factorial analyses of variance were performed to test for differences in the number of species among. maritime (oceanic and Mediterranean), continental and mountainous. In addition, in order to evaluate the global influence of climate on algal and cyanobacterial colonisation, the sampled sites were classified into one of the four climates occurring in France, viz. The data were extracted from a report (Beguin, 1986). the time for which external building surfaces remained wet. ‘Wetness’ was also included in the set of environmental parameters, i.e. Taking into account that the colonisation of buildings may take several years, and in order to avoid inter-year fluctuation, the selected data set consisted of an average of the data recorded each month, for 30 years. The following climate data were obtained from M ́t ́o France for each one of the 18 sampled locations: minimal and maximal temperature, precipitation, relative hygrometry, insulation, snow and wind speed. beneath a leaking gutter, a zone of water rejection on bases of walls) than the others (fa ̧ade only subjected to rainfalls). A parameter of micro- humidity was also defined to differentiate zones of the building supporting local higher moisture (e.g. Substrata sampled included minerals (one-coat rendering mortar, clas- sical mortar, concrete, cement, stone) and organics (paint, organic finish, plastic). the altitude and distance from the sea, the presence of vegetation close to the building, the geographical exposition of the colonised substratum, and the nature of the substratum. Environmental parameters were collected for each sample, viz. While identifying the specimens composing the sampled biofilms, a species was noted as dominant when it represented more than 50% of the total biomass. Identifications were based on taxonomic criteria established by Geitler (1932), Bourrelly (1966 1985), Kom ́rek and Fott (1983), Ettl and G ̈rtner (1995), Kom ́rek and Anagnostidis (1999), and John et al. In order to highlight some of the traits used in taxonomy, lugol solution and India ink were employed.
Primary cultures were used to obtain unialgal replicates using the streaking plate method. Cultures were grown in a chamber with a photo- period of 16 h/8 h light/dark, a photosynthetic photon flux (PPF) of 15 + 3 m mol m 7 2 s 7 1 and a temperature of 20 + 0.5 8 C.
Another portion of material was placed into Petri dishes with agar Bold Basal medium to enhance culture growth. In order to analyse the algal and cyanobacterial composition of the samples, a portion of the material was placed for 1 – 2 h in water to ease the removal of the biofilm, thus enabling a primary study of the populations in their natural state. One sample from each of 71 buildings around France, from 18 different locations representing diverse climates, was collected by removing a portion of the colonised substratum ( Figure 1). A collection of biofilms around France was carried out, intending to be representative of the great diversity of occurring stains, in terms of colour (by hypothesis, the variety of colours observed was due to different algal and cyanobacterial composition), distribution on the envelope of the building and the nature of the colonised substrata. It was the goal of the present study to identify certain key parameters that may favour biofilm formation on buildings using multivariate analysis. Although an understanding of the factors influencing algal and cyanobacterial colonisation appears to be fundamental in preventing this situation, there has been no complete work based on statistical analysis of colonised buildings in France. In Spain, a study was carried out to investigate environmental factors such as pH, availability of nitrogen and moisture, structural com- plexity and human presence, in their ability to affect the distribution of lichens (Prieto et al.
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