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Drought is one of the most significant extreme event facing the world, affecting the society and the environment. Located in SE Romania, Dobrogea Region is characterized by a temperate climate with strong continental influences, being affected by drought episodes which cause significant damages and economic costs over extensive agricultural areas. Risk reduction, continuous vegetation monitoring, and management implementation are facilitated by complementary use of vegetation indices and biophysical parameters derived from satellite products (gridded data) within-situ data (point data). The paper focuses on:i) evaluating the extent and intensity of drought in Dobrogea, Romania, based on Normalized Difference Drought Index (NDDI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR); ii) fires analysis, based on the Thermal Anomalies/Fire locations product (MCD14DL); iii)the correlation between the fires with the NDDI; iv) and the correlation between fires with the Land Surface Temperature (LST) product. The vegetation indices, biophysical parameters and fires are computed from Moderate Resolution Imaging Spectroradiometer (MODIS) daily and eight days’ synthesis products, during 22th of March - 29th of August 2000-2015. The results highlight the areas most affected by drought (moderate, severe and extreme) and fires in the Dobrogea.

In this study, an evaluation of food waste generation was conducted, using images taken before and after the daily meals of people aged between 20 and 30 years in Serbia, for the period between January 1st and April 31st in 2022. A convolutional neural network (CNN) was employed for the tasks of recognizing food images before the meal and estimating the percentage of food waste according to the photographs taken. Keeping in mind the vast variates and types of food available, the image recognition and validation of food items present a generally very challenging task. Nevertheless, deep learning has recently been shown to be a very potent image recognition procedure, while CNN presents a state-of-the-art method of deep learning. The CNN technique was implemented to the food detection and food waste estimation tasks throughout the parameter optimization procedure. The images of the most frequently encountered food items were collected from the internet to create an image dataset, covering 157 food categories, which was used to evaluate recognition performance. Each category included between 50 and 200 images, while the total number of images in the database reached 23,552. The CNN model presented good prediction capabilities, showing an accuracy of 0.988 and a loss of 0.102, after the network training cycle. The average food waste per meal, in the frame of the analysis in Serbia, was 21.3%, according to the images collected for food waste evaluation.

Laser scanning is a new measurement technique through which can be recorded, fully automatic with high precision and speed. In documenting of existing buildings and facilities, knowing the geometry of the object is the most important. Old university buildings are icons from the past that exist in present time. In order to preserve this heritage for the future generation, recording and documenting of university buildings are required. With the development of information system and data collection technique, it is possible to create a 3D digital model. This 3D information plays an important role in recording and documenting old buildings. The purpose of this paper is to achieve the objective, namely, achieving 3D digital model of a university building located in the USAMV Bucharest campus. Examining the building, which has existed for over 80 years has a fairly high level of degradation, requiring investigation overall. Digital model has the role to graphically restore, as closely as possible the objective scanned, obtaining all the necessary information for this old building. Through this research, a proper database for storing and documenting the university buildings conservation data will be developed in order to attempt to register them in UNESCO patrimony.