scholarly journals Deep learning-based waste detection in natural and urban environments

2022 ◽  
Vol 138 ◽  
pp. 274-284
Author(s):  
Sylwia Majchrowska ◽  
Agnieszka Mikołajczyk ◽  
Maria Ferlin ◽  
Zuzanna Klawikowska ◽  
Marta A. Plantykow ◽  
...  
Keyword(s):  
Deep Learning ◽  
Urban Environments

scholarly journals Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7716
Author(s):  
Krzysztof K. Cwalina ◽  
Piotr Rajchowski ◽  
Alicja Olejniczak ◽  
Olga Błaszkiewicz ◽  
Robert Burczyk
Keyword(s):  
Deep Learning ◽  
Interface Design ◽  
Mean Squared Error ◽  
Radio Channel ◽  
Computational Cost ◽  
Urban Environments ◽  
Data Allocation ◽  
Urban Networks ◽  
Radio Interface ◽  
Channel State Estimation

Following the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for estimating the radio channel parameters of the LTE (Long Term Evolution) radio interface is presented. It was proved that the deep learning approach provides a significant gain (almost 40%) with 10.7% compared to the linear model with the lowest RMSE (Root Mean Squared Error) 17.01%. The solution can be adopted as a part of the data allocation algorithm implemented in the telemetry devices equipped with the 4G radio interface, or, after the adjustment, the NB-IoT (Narrowband Internet of Things), to maximize the reliability of the services in harsh indoor or urban environments. Presented results also prove the existence of the inverse proportional dependence between the number of hidden layers and the number of historical samples in terms of the obtained RMSE. The increase of the historical data memory allows using models with fewer hidden layers while maintaining a comparable RMSE value for each scenario, which reduces the total computational cost.

scholarly journals AN EFFICIENT DEEP LEARNING APPROACH FOR GROUND POINT FILTERING IN AERIAL LASER SCANNING POINT CLOUDS

2021 ◽  
Vol XLIII-B1-2021 ◽  
pp. 31-38
Author(s):  
A. Nurunnabi ◽  
F. N. Teferle ◽  
J. Li ◽  
R. C. Lindenbergh ◽  
A. Hunegnaw
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Ground Surface ◽  
Point Clouds ◽  
Urban Environments ◽  
Training Data ◽  
Ground Point ◽  
End To End

Abstract. Ground surface extraction is one of the classic tasks in airborne laser scanning (ALS) point cloud processing that is used for three-dimensional (3D) city modelling, infrastructure health monitoring, and disaster management. Many methods have been developed over the last three decades. Recently, Deep Learning (DL) has become the most dominant technique for 3D point cloud classification. DL methods used for classification can be categorized into end-to-end and non end-to-end approaches. One of the main challenges of using supervised DL approaches is getting a sufficient amount of training data. The main advantage of using a supervised non end-to-end approach is that it requires less training data. This paper introduces a novel local feature-based non end-to-end DL algorithm that generates a binary classifier for ground point filtering. It studies feature relevance, and investigates three models that are different combinations of features. This method is free from the limitations of point clouds’ irregular data structure and varying data density, which is the biggest challenge for using the elegant convolutional neural network. The new algorithm does not require transforming data into regular 3D voxel grids or any rasterization. The performance of the new method has been demonstrated through two ALS datasets covering urban environments. The method successfully labels ground and non-ground points in the presence of steep slopes and height discontinuity in the terrain. Experiments in this paper show that the algorithm achieves around 97% in both F1-score and model accuracy for ground point labelling.

scholarly journals TreeVibes: Modern tools for Global Monitoring of Trees Against Borers

2020 ◽  
Author(s):  
Iraklis Rigakis ◽  
Ilyas Potamitis ◽  
Nicolas Alexander Tatlas ◽  
Stelios M. Potirakis ◽  
Stavros Ntalampiras
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Cost Effective ◽  
Automatic Monitoring ◽  
Urban Environments ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Longhorn Beetles ◽  
Cloud Server ◽  
Wood Boring

Is there a wood-feeding insect inside a tree or wooden structure? We investigate several ways on how deep learning approaches can massively scan recordings of vibrations stemming from probed trees to infer their infestation state with wood-boring insects that feed and move inside wood. The recordings come from remotely controlled devices that sample the internal soundscape of trees on a 24/7 basis and wirelessly transmit brief recordings of the registered vibrations to a cloud server. We discuss the different sources of vibrations that can be picked up from trees in urban environments and how deep learning methods can focus on those originating from borers. Our goal is to match the problem of the accelerated—due to global trade and climate change— establishment of invasive xylophagus insects by increasing the capacity of inspection agencies. We aim at introducing permanent, cost-effective, automatic monitoring of trees based on deep learning techniques, in commodity entry point as well as in wild, urban and cultivated areas in order to effect large-scale, sustainable pest-risk analysis and management of wood boring insects such as those from the Cerambycidae family (longhorn beetles).

scholarly journals Recognition of Urban Sound Events using Deep Context-Aware Feature Extractors and Handcrafted Features

2018 ◽  
Author(s):  
Theodoros Giannakopoulos ◽  
Stavros Perantonis
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Urban Environments ◽  
Complex Data ◽  
Context Aware ◽  
Contextual Knowledge ◽  
Feature Representations ◽  
Audio Features ◽  
Significant Performance ◽  
Core Idea

This paper proposes a method for recognizing audio events in urban environments that combines handcrafted audio features with a deep learning architectural scheme (Convolutional Neural Networks, CNNs), which has been trained to distinguish between different audio context classes. The core idea is to use the CNNs as a method to extract context-aware deep audio features that can offer supplementary feature representations to any soundscape analysis classification task. Towards this end, the CNN is trained on a database of audio samples which are annotated in terms of their respective "scene" (e.g. train, street, park), and then it is combined with handcrafted audio features in an early fusion approach, in order to recognize the audio event of an unknown audio recording. Detailed experimentation proves that the proposed context-aware deep learning scheme, when combined with the typical handcrafted features, leads to a significant performance boosting in terms of classification accuracy. The main contribution of this work is the demonstration that transferring audio contextual knowledge using CNNs as feature extractors can significantly improve the performance of the audio classifier, without need for CNN training (a rather demanding process that requires huge datasets and complex data augmentation procedures).

Deep Learning for Flood Forecasting and Monitoring in Urban Environments

2019 ◽  
Author(s):  
Charalampos Karyotis ◽  
Tomasz Maniak ◽  
Faiyaz Doctor ◽  
Rahat Iqbal ◽  
Vasile Palade ◽  
...  
Keyword(s):  
Deep Learning ◽  
Flood Forecasting ◽  
Urban Environments

scholarly journals Semantic Segmentation of Tree-Canopy in Urban Environment with Pixel-Wise Deep Learning

2021 ◽  
Vol 13 (16) ◽  
pp. 3054
Author(s):  
José Augusto Correa Martins ◽  
Keiller Nogueira ◽  
Lucas Prado Osco ◽  
Felipe David Georges Gomes ◽  
Danielle Elis Garcia Furuya ◽  
...  
Keyword(s):  
Deep Learning ◽  
Urban Areas ◽  
Urban Forests ◽  
Semantic Segmentation ◽  
Urban Environments ◽  
Tree Canopy ◽  
Environmental Benefits ◽  
Urban Systems ◽  
Convolutional Network ◽  
Environmental Technologies

Urban forests are an important part of any city, given that they provide several environmental benefits, such as improving urban drainage, climate regulation, public health, biodiversity, and others. However, tree detection in cities is challenging, given the irregular shape, size, occlusion, and complexity of urban areas. With the advance of environmental technologies, deep learning segmentation mapping methods can map urban forests accurately. We applied a region-based CNN object instance segmentation algorithm for the semantic segmentation of tree canopies in urban environments based on aerial RGB imagery. To the best of our knowledge, no study investigated the performance of deep learning-based methods for segmentation tasks inside the Cerrado biome, specifically for urban tree segmentation. Five state-of-the-art architectures were evaluated, namely: Fully Convolutional Network; U-Net; SegNet; Dynamic Dilated Convolution Network and DeepLabV3+. The experimental analysis showed the effectiveness of these methods reporting results such as pixel accuracy of 96,35%, an average accuracy of 91.25%, F1-score of 91.40%, Kappa of 82.80% and IoU of 73.89%. We also determined the inference time needed per area, and the deep learning methods investigated after the training proved to be suitable to solve this task, providing fast and effective solutions with inference time varying from 0.042 to 0.153 minutes per hectare. We conclude that the semantic segmentation of trees inside urban environments is highly achievable with deep neural networks. This information could be of high importance to decision-making and may contribute to the management of urban systems. It should be also important to mention that the dataset used in this work is available on our website.

scholarly journals TreeVibes: Modern Tools for Global Monitoring of Trees for Borers

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 271-285
Author(s):  
Iraklis Rigakis ◽  
Ilyas Potamitis ◽  
Nicolaos-Alexandros Tatlas ◽  
Stelios M. Potirakis ◽  
Stavros Ntalampiras
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Cost Effective ◽  
Automatic Monitoring ◽  
Urban Environments ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Longhorn Beetles ◽  
Entry Points ◽  
Wood Boring

Is there a wood-feeding insect inside a tree or wooden structure? We investigate several ways of how deep learning approaches can massively scan recordings of vibrations stemming from probed trees to infer their infestation state with wood-boring insects that feed and move inside wood. The recordings come from remotely controlled devices that sample the internal soundscape of trees on a 24/7 basis and wirelessly transmit brief recordings of the registered vibrations to a cloud server. We discuss the different sources of vibrations that can be picked up from trees in urban environments and how deep learning methods can focus on those originating from borers. Our goal is to match the problem of the accelerated—due to global trade and climate change— establishment of invasive xylophagus insects by increasing the capacity of inspection agencies. We aim at introducing permanent, cost-effective, automatic monitoring of trees based on deep learning techniques, in commodity entry points as well as in wild, urban and cultivated areas in order to effect large-scale, sustainable pest-risk analysis and management of wood boring insects such as those from the Cerambycidae family (longhorn beetles).

scholarly journals EspiNet V2: a region based deep learning model for detecting motorcycles in urban scenarios

DYNA ◽  
2019 ◽  
Vol 86 (211) ◽  
pp. 317-326
Author(s):  
Jorge Ernesto Espinosa Oviedo ◽  
Sergio A Velastín ◽  
John William Branch Bedoya
Keyword(s):  
Deep Learning ◽  
Traffic Control ◽  
Learning Model ◽  
Urban Environments ◽  
Average Precision ◽  
Moving Camera ◽  
Model Based ◽  
End To End ◽  
Deep Learning Model

This paper presents “EspiNet V2” a Deep Learning model, based on the region-based detector Faster R-CNN. The model is used for the detection of motorcycles in urban environments, where occlusion is likely. For training, two datasets are used: the Urban Motorbike Dataset (UMD-10K) of 10,000 annotated images, and the new SMMD (Secretaría de Movilidad Motorbike Dataset), of 5,000 images captured from the Traffic Control CCTV System in Medellín (Colombia). Results achieved on the UMD-10K dataset reach 88.8% in average precision (AP) even when 60% motorcycles were occluded, and the images were captured from a low angle and a moving camera. Meanwhile, an AP of 79.5% is reached for SSMD. EspiNet V2 outperforms popular models such as YOLO V3 and Faster R-CNN (VGG16 based) trained end-to-end for those datasets

scholarly journals DEEP NEURAL NETWORKS FOR ABOVE-GROUND DETECTION IN VERY HIGH SPATIAL RESOLUTION DIGITAL ELEVATION MODELS

2015 ◽  
Vol II-3/W4 ◽  
pp. 103-110 ◽  
Author(s):  
D. Marmanis ◽  
F. Adam ◽  
M. Datcu ◽  
T. Esch ◽  
U. Stilla
Keyword(s):  
Deep Learning ◽  
Urban Environments ◽  
Convergence Time ◽  
Classification Problems ◽  
Learning Techniques ◽  
Digital Elevation ◽  
Very High Spatial Resolution ◽  
Novel Method ◽  
Dongying City ◽  
The City

Deep Learning techniques have lately received increased attention for achieving state-of-the-art results in many classification problems, including various vision tasks. In this work, we implement a Deep Learning technique for classifying above-ground objects within urban environments by using a Multilayer Perceptron model and VHSR DEM data. In this context, we propose a novel method called M-ramp which significantly improves the classifier’s estimations by neglecting artefacts, minimizing convergence time and improving overall accuracy. We support the importance of using the M-ramp model in DEM classification by conducting a set of experiments with both quantitative and qualitative results. Precisely, we initially train our algorithm with random DEM tiles and their respective point-labels, considering less than 0.1% over the test area, depicting the city center of Munich (25 km<sup>2</sup>). Furthermore with no additional training, we classify two much larger unseen extents of the greater Munich area (424 km<sup>2</sup>) and Dongying city, China (257 km<sup>2</sup>) and evaluate their respective results for proving knowledge-transferability. Through the use of M-ramp, we were able to accelerate the convergence by a magnitude of 8 and achieve a decrease in above-ground relative error by 24.8% and 5.5% over the different datasets.

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