Activity of vehicles in the bus rapid transit system Metrobús in Mexico City

In this paper, we analyze a massive dataset with registers of the movement of vehicles in the bus rapid transit system Metrobús in Mexico City from February 2020 to April 2021. With these records and a division of the system into 214 geographical regions (segments), we characterize the vehicles’ activity through the statistical analysis of speeds in each zone. We use the Kullback–Leibler distance to compare the movement of vehicles in each segment and its evolution. The results for the dynamics in different zones are represented as a network where nodes define segments of the system Metrobús and edges describe similarity in the activity of vehicles. Community detection algorithms in this network allow the identification of patterns considering different levels of similarity in the distribution of speeds providing a framework for unsupervised classification of the movement of vehicles. The methods developed in this research are general and can be implemented to describe the activity of different transportation systems with detailed records of the movement of users or vehicles.

Mapping Glacier Ablation With a UAV in the North Cascades: A Structure-from-Motion Approach

The glaciers of the North Cascades have experienced mass loss and terminus retreat due to climate change. The meltwater from these glaciers provides a flux of cold glacier meltwater into the river systems, which supports salmon spawning during the late summer dry season. The Nooksack Indian Tribe monitors the outlet flow of the Sholes Glacier within the North Cascades range with the goal of understanding the health of the glacier and the ability of the Tribe to continue to harvest sustainable populations of salmon. This study compares the UAV derived glacier ablation with the discharge data collected by the Tribe. We surveyed the Sholes Glacier twice throughout the 2020 melt season and, using Structure-from-Motion technology, generated high resolution multispectral orthomosaics and Digital Elevation Models (DEMs) of the glacier on each of the survey dates. The DEMs were differenced to reveal the surface height change of the glacier. The spectral data of the orthomosaics were used to conduct IsoData unsupervised classification. This process divided the survey area into Snow, Ice, and Rock classes that were then used to attribute the surface height changes of the DEMs to either snow or ice melt. The analysis revealed the glacier lost an average thickness of −0.132 m per day (m d−1) with snow and ice losing thickness at similar rates, −0.130 m d−1 and −0.132 m d−1 respectively. DEM differencing reveals that a total of −550,161 ± 45,206 m3 water equivalent (w.e.) was discharged into Wells Creek between the survey dates whereas the stream gauge station measured a total discharge of 350,023 m3. This study demonstrates the ability to spectrally classify the UAV data and derive discharge measurements while evaluating the small-scale spatial variability of glacier melt. Assessing ablation in small alpine glaciers is of great importance to downstream communities, like the Nooksack Indian Tribe who seek to understand the magnitude and timing of glacier melt in order to better protect their salmon populations. With this paper, we provide a baseline for future glacier monitoring and the potential to connect the snow surface properties with the rate of snow melt into a warming future.

Monitoring of Land Degradation in Alluvial Plain in Iraq by Using Geomatics Techniques

Iraq faces a major environmental problem represented by severe deterioration, which threatens its food security. Many natural and human factors combine to make it, and it has dire environmental, economic, social and cultural consequences, most notably the loss of productive lands, the movement of sand dunes, severe sand and dust storms, and the resulting increase in air pollution. This study attempts to identify the development of the problem, analyze its causes and consequences, and propose a number of solutions to address it. In this article Remote Sensing techniques have been used to monitoring land degradation in ( Alluvial Plain ) of Iraq for the stage (1976 - 2021) using different sources of data such as satellite images (Landsat 1-5 MSS 1976, Landsat 5 1996 TM, Landsat8 2016 and sentinel 2 2021), also more than one software was used such as ENVI 5.3 and Erdas image 2015 to extract information from above images, Erdas imagine 2015 was use to sub set area of study, layer stack, merge resolution and classification stage, Arc GIS 10.7 use to make database and maps production), the article used supervise and unsupervised classification techniques to obtain the results, the article indicated that there is a big problem in the year 1976, this problem almost disappeared in the second station of work 1996, but it returned back after that through the results for the years 2016 and 2021. Finally, the article found a deterioration in the soil class during the stages from 2016 (988.547 Km2) to 2021(1342.398 Km2) and a decrease in the area of vegetation cover from (1931.596 Km2) in (2016) to (1632.695 Km2) in (2021).

Application of satellite imagery to update depth-area-volume relationships in reservoirs in the semiarid region of Northeast Brazil

ABSTRACT Reservoirs are the primary source of water supply in the semiarid region of Pernambuco state, Brazil, because of the constant water scarcity affecting this region. Knowledge of the amount of water available is essential for the effective management of water resources. The volume of water stored in the reservoirs is calculated using the depth-area-volume relationship. However, in most reservoirs in the semiarid region, this relationship is currently out of date. Therefore, the objective of this study was to explore the potential and limitations of the application of the ISODATA unsupervised classification method to calculate the depth-area-volume relationships of reservoirs in the semiarid region of Pernambuco, Brazil. The application of the ISODATA method was evaluated in three reservoirs in the state of Pernambuco, i.e., Poço da Cruz, Barra do Juá, and Jucazinho. The results were compared with the updated curves of reservoirs obtained from bathymetry and recent LiDAR surveys. The ISODATA method presented satisfactory results for the three reservoirs analyzed. The mean absolute error of the volume in Poço da Cruz and Barra do Juá was lower than 1% of the maximum capacity. The use of the ISODATA method meant that the surface area underestimation tendency in the Poço da Cruz reservoir was less than when spectral indices were used.

Comparing supervised and unsupervised approaches to multimodal emotion recognition

We investigated emotion classification from brief video recordings from the GEMEP database wherein actors portrayed 18 emotions. Vocal features consisted of acoustic parameters related to frequency, intensity, spectral distribution, and durations. Facial features consisted of facial action units. We first performed a series of person-independent supervised classification experiments. Best performance (AUC = 0.88) was obtained by merging the output from the best unimodal vocal (Elastic Net, AUC = 0.82) and facial (Random Forest, AUC = 0.80) classifiers using a late fusion approach and the product rule method. All 18 emotions were recognized with above-chance recall, although recognition rates varied widely across emotions (e.g., high for amusement, anger, and disgust; and low for shame). Multimodal feature patterns for each emotion are described in terms of the vocal and facial features that contributed most to classifier performance. Next, a series of exploratory unsupervised classification experiments were performed to gain more insight into how emotion expressions are organized. Solutions from traditional clustering techniques were interpreted using decision trees in order to explore which features underlie clustering. Another approach utilized various dimensionality reduction techniques paired with inspection of data visualizations. Unsupervised methods did not cluster stimuli in terms of emotion categories, but several explanatory patterns were observed. Some could be interpreted in terms of valence and arousal, but actor and gender specific aspects also contributed to clustering. Identifying explanatory patterns holds great potential as a meta-heuristic when unsupervised methods are used in complex classification tasks.

A New Method (MINDED-BA) for Automatic Detection of Burned Areas Using Remote Sensing

This work presents a change detection method (MINDED-BA) for determining burned extents from multispectral remote sensing imagery. It consists of a development of a previous model (MINDED), originally created to estimate flood extents, combining a multi-index image-differencing approach and the analysis of magnitudes of the image-differencing statistics. The method was implemented, using Landsat and Sentinel-2 data, to estimate yearly burn extents within a study area located in northwest central Portugal, from 2000–2019. The modelling workflow includes several innovations, such as preprocessing steps to address some of the most important sources of error mentioned in the literature, and an optimal bin number selection procedure, the latter being the basis for the threshold selection for the classification of burn-related changes. The results of the model have been compared to an official yearly-burn-extent database and allow verifying the significant improvements introduced by both the pre-processing procedures and the multi-index approach. The high overall accuracies of the model (ca. 97%) and its levels of automatization (through open-source software) indicate potential for being a reliable method for systematic unsupervised classification of burned areas.

Multimedia Detection and Processing of Remote-Sensing Image of Small Target Combined with Variable Neighborhood Search Algorithm

The development of scientific satellites has made it a reality for people to view the Earth from the sky. However, due to the resolution of the image obtained, the effective and accurate interpretation of remote-sensing images has always been one of the goals pursued by the industry. In this paper, we merge the variable neighborhood search algorithm, reduce the accuracy of remote-sensing images, clean the invalid information of the data, use unsupervised classification methods to quickly locate small targets, use it as verification information, compare and select the image data through sample information, distinguish the background and target results, and get stable detection results. Practice shows that this method can effectively detect small targets in remote-sensing images.