212 research outputs found
A review of machine learning applications in wildfire science and management
Artificial intelligence has been applied in wildfire science and management
since the 1990s, with early applications including neural networks and expert
systems. Since then the field has rapidly progressed congruently with the wide
adoption of machine learning (ML) in the environmental sciences. Here, we
present a scoping review of ML in wildfire science and management. Our
objective is to improve awareness of ML among wildfire scientists and managers,
as well as illustrate the challenging range of problems in wildfire science
available to data scientists. We first present an overview of popular ML
approaches used in wildfire science to date, and then review their use in
wildfire science within six problem domains: 1) fuels characterization, fire
detection, and mapping; 2) fire weather and climate change; 3) fire occurrence,
susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6)
fire management. We also discuss the advantages and limitations of various ML
approaches and identify opportunities for future advances in wildfire science
and management within a data science context. We identified 298 relevant
publications, where the most frequently used ML methods included random
forests, MaxEnt, artificial neural networks, decision trees, support vector
machines, and genetic algorithms. There exists opportunities to apply more
current ML methods (e.g., deep learning and agent based learning) in wildfire
science. However, despite the ability of ML models to learn on their own,
expertise in wildfire science is necessary to ensure realistic modelling of
fire processes across multiple scales, while the complexity of some ML methods
requires sophisticated knowledge for their application. Finally, we stress that
the wildfire research and management community plays an active role in
providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table
Project RISE: Recognizing Industrial Smoke Emissions
Industrial smoke emissions pose a significant concern to human health. Prior
works have shown that using Computer Vision (CV) techniques to identify smoke
as visual evidence can influence the attitude of regulators and empower
citizens to pursue environmental justice. However, existing datasets are not of
sufficient quality nor quantity to train the robust CV models needed to support
air quality advocacy. We introduce RISE, the first large-scale video dataset
for Recognizing Industrial Smoke Emissions. We adopted a citizen science
approach to collaborate with local community members to annotate whether a
video clip has smoke emissions. Our dataset contains 12,567 clips from 19
distinct views from cameras that monitored three industrial facilities. These
daytime clips span 30 days over two years, including all four seasons. We ran
experiments using deep neural networks to establish a strong performance
baseline and reveal smoke recognition challenges. Our survey study discussed
community feedback, and our data analysis displayed opportunities for
integrating citizen scientists and crowd workers into the application of
Artificial Intelligence for social good.Comment: Technical repor
ΠΠΠΠΠ ΠΠ’Π ΠΠΠΠΠΠΠ ΠΠΠΠΠΠΠ§ΠΠ‘ΠΠΠ₯ Π’ΠΠΠ‘Π’Π£Π
Recognizing dynamic patterns based on visual processing is significant for many applications such as remote monitoring for the prevention of natural disasters, e.g. forest fires, various types of surveillance, e.g. traffic monitoring, background subtraction in challenging environments, e.g. outdoor scenes with vegetation, homeland security applications and scientific studies of animal behavior. In the context of surveillance, recognizing dynamic patterns is of significance to isolate activities of interest (e.g. fire) from distracting background (e.g. windblown vegetation and changes in scene illumination).Methods: pattern recognition, computer vision.Results: This paper presents video based image processing algorithm with samples usually containing a cluttered background. According to the spatiotemporal features, four categorized groups were formulated. Dynamic texture recognition algorithm refers image objects to one of this group. Motion, color, facial, energy Laws and ELBP features are extracted for dynamic texture categorization. Classification based on boosted random forest.Practical relevance: Experimental results show that the proposed method is feasible and effective for video-based dynamic texture categorization. Averaged classification accuracy on the all video images is 95.2%.ΠΠΎΡΡΠ°Π½ΠΎΠ²ΠΊΠ° ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ: ΠΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΠ΅ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅ΠΊΡΡΡΡ Π½Π° Π²ΠΈΠ΄Π΅ΠΎΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΡ
Π² Π½Π°ΡΡΠΎΡΡΠ΅Π΅ Π²ΡΠ΅ΠΌΡ Π½Π°Ρ
ΠΎΠ΄ΠΈΡ Π²ΡΠ΅ Π±ΠΎΠ»Π΅Π΅ ΡΠΈΡΠΎΠΊΠΎΠ΅ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ Π² ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠ³ΠΎ Π·ΡΠ΅Π½ΠΈΡ. ΠΠ°ΠΏΡΠΈΠΌΠ΅Ρ, ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΠ΅ Π΄ΡΠΌΠ° ΠΈ ΠΏΠ»Π°ΠΌΠ΅Π½ΠΈ Π² ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
ΡΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π°, Π°Π½Π°Π»ΠΈΠ· Π°Π²ΡΠΎΠΌΠΎΠ±ΠΈΠ»ΡΠ½ΠΎΠ³ΠΎ ΡΡΠ°ΡΠΈΠΊΠ° ΠΏΡΠΈ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π΅ Π·Π°Π³ΡΡΠΆΠ΅Π½Π½ΠΎΡΡΠΈ Π΄ΠΎΡΠΎΠ³, ΠΈ Π² Π΄ΡΡΠ³ΠΈΡ
ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
. ΠΠΎΠΈΡΠΊ ΠΎΠ±ΡΠ΅ΠΊΡΠ° ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠ° Π½Π° Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΎΠΌ ΡΠΎΠ½Π΅ ΡΠ°ΡΡΠΎ Π±ΡΠ²Π°Π΅Ρ Π·Π°ΡΡΡΠ΄Π½Π΅Π½ Π·Π° ΡΡΠ΅Ρ ΠΏΠΎΡ
ΠΎΠΆΠΈΡ
ΡΠ΅ΠΊΡΡΡΡΠ½ΡΡ
ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² ΠΈΠ»ΠΈ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ Ρ ΡΠΎΠ½Π° ΠΈ ΠΈΡΠΊΠΎΠΌΠΎΠ³ΠΎ ΠΎΠ±ΡΠ΅ΠΊΡΠ°. Π ΡΠ²ΡΠ·ΠΈ Ρ ΡΡΠΈΠΌ Π²ΠΎΠ·Π½ΠΈΠΊΠ°Π΅Ρ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅ΠΊΡΡΡΡ Π΄Π»Ρ Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠ° Π½Π° Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΎΠΌ ΡΠΎΠ½Π΅.ΠΠ΅ΡΠΎΠ΄Ρ: ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΠ΅ ΠΎΠ±ΡΠ°Π·ΠΎΠ², ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠ΅ Π·ΡΠ΅Π½ΠΈΠ΅.Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ: Π Π΄Π°Π½Π½ΠΎΠΉ ΡΠ°Π±ΠΎΡΠ΅ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΡΡΡ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ° Π²ΠΈΠ΄Π΅ΠΎΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΡΠΎΠ΄Π΅ΡΠΆΠ°ΡΠΈΡ
ΠΎΠ±ΡΠ΅ΠΊΡΡ Ρ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΈΠΌ ΠΏΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠ΅ΠΌ Π½Π° Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΎΠΌ ΡΠΎΠ½Π΅, ΡΠ°ΠΊΠΈΠ΅ ΠΊΠ°ΠΊ Π²ΠΎΠ΄Π°, ΡΡΠΌΠ°Π½, ΠΏΠ»Π°ΠΌΡ, ΡΠ΅ΠΊΡΡΠΈΠ»Ρ Π½Π° Π²Π΅ΡΡΡ ΠΈ Π΄Ρ. Π Π°Π·ΡΠ°Π±ΠΎΡΠ°Π½ Π°Π»Π³ΠΎΡΠΈΡΠΌ ΠΎΡΠ½Π΅ΡΠ΅Π½ΠΈΡ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² Π²ΠΈΠ΄Π΅ΠΎΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ ΠΊ ΠΎΠ΄Π½ΠΎΠΉ ΠΈΠ· ΡΠ΅ΡΡΡΠ΅Ρ
ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΡΡ
ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΠΉ. ΠΠ·Π²Π»Π΅ΠΊΠ°ΡΡΡΡ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΈ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ, ΡΠ²Π΅ΡΠΎΠ²ΡΠ΅ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ, ΡΡΠ°ΠΊΡΠ°Π»ΡΠ½ΠΎΡΡΠΈ, ΡΠ½Π΅ΡΠ³Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΈ ΠΠ°ΡΠ°, ΡΡΡΠΎΡΡΡΡ ELBP-Π³ΠΈΡΡΠΎΠ³ΡΠ°ΠΌΠΌΡ. Π ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΡΠ° ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ Π±ΡΡΡΠΈΠ½Π³ΠΎΠ²ΡΠΉ ΡΠ»ΡΡΠ°ΠΉΠ½ΡΠΉ Π»Π΅Ρ.ΠΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠ°Ρ Π·Π½Π°ΡΠΈΠΌΠΎΡΡΡ: Π Π°Π·ΡΠ°Π±ΠΎΡΠ°Π½ ΠΌΠ΅ΡΠΎΠ΄, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠΈΠΉ ΡΠ°Π·Π΄Π΅Π»ΠΈΡΡ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΠ΅ΠΊΡΡΡΡ Π½Π° ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΠΈ: ΠΏΠΎ ΡΠΈΠΏΡ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ (ΠΏΠ΅ΡΠΈΠΎΠ΄ΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΠΈ Ρ
Π°ΠΎΡΠΈΡΠ½ΠΎΠ΅) ΠΈ ΡΠΈΠΏΡ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠ° (ΠΏΡΠΈΡΠΎΠ΄Π½ΡΠ΅ ΠΈ ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΡΠ΅). ΠΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠΆΠ΄Π°ΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° Π΄Π»Ρ ΠΎΡΠ½Π΅ΡΠ΅Π½ΠΈΡ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ ΠΊ ΡΠΎΠΉ ΠΈΠ»ΠΈ ΠΈΠ½ΠΎΠΉ ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΠΈ. Π‘ΡΠ΅Π΄Π½ΡΡ ΡΠΎΡΠ½ΠΎΡΡΡ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠΎΡΡΠ°Π²ΠΈΠ»Π° 95.2%
Verification of Smoke Detection in Video Sequences Based on Spatio-temporal Local Binary Patterns
AbstractThe early smoke detection in outdoor scenes using video sequences is one of the crucial tasks of modern surveillance systems. Real scenes may include objects that are similar to smoke with dynamic behavior due to low resolution cameras, blurring, or weather conditions. Therefore, verification of smoke detection is a necessary stage in such systems. Verification confirms the true smoke regions, when the regions similar to smoke are already detected in a video sequence. The contributions are two-fold. First, many types of Local Binary Patterns (LBPs) in 2D and 3D variants were investigated during experiments according to changing properties of smoke during fire gain. Second, map of brightness differences, edge map, and Laplacian map were studied in Spatio-Temporal LBP (STLBP) specification. The descriptors are based on histograms, and a classification into three classes such as dense smoke, transparent smoke, and non-smoke was implemented using Kullback-Leibler divergence. The recognition results achieved 96β99% and 86β94% of accuracy for dense smoke in dependence of various types of LPBs and shooting artifacts including noise
Multi-teacher knowledge distillation as an effective method for compressing ensembles of neural networks
Deep learning has contributed greatly to many successes in artificial
intelligence in recent years. Today, it is possible to train models that have
thousands of layers and hundreds of billions of parameters. Large-scale deep
models have achieved great success, but the enormous computational complexity
and gigantic storage requirements make it extremely difficult to implement them
in real-time applications. On the other hand, the size of the dataset is still
a real problem in many domains. Data are often missing, too expensive, or
impossible to obtain for other reasons. Ensemble learning is partially a
solution to the problem of small datasets and overfitting. However, ensemble
learning in its basic version is associated with a linear increase in
computational complexity. We analyzed the impact of the ensemble
decision-fusion mechanism and checked various methods of sharing the decisions
including voting algorithms. We used the modified knowledge distillation
framework as a decision-fusion mechanism which allows in addition compressing
of the entire ensemble model into a weight space of a single model. We showed
that knowledge distillation can aggregate knowledge from multiple teachers in
only one student model and, with the same computational complexity, obtain a
better-performing model compared to a model trained in the standard manner. We
have developed our own method for mimicking the responses of all teachers at
the same time, simultaneously. We tested these solutions on several benchmark
datasets. In the end, we presented a wide application use of the efficient
multi-teacher knowledge distillation framework. In the first example, we used
knowledge distillation to develop models that could automate corrosion
detection on aircraft fuselage. The second example describes detection of smoke
on observation cameras in order to counteract wildfires in forests.Comment: Doctoral dissertation in the field of computer science, machine
learning. Application of knowledge distillation as aggregation of ensemble
models. Along with several uses. 140 pages, 67 figures, 13 table
Horizontal Review on Video Surveillance for Smart Cities: Edge Devices, Applications, Datasets, and Future Trends
The automation strategy of todayβs smart cities relies on large IoT (internet of Things) systems that collect big data analytics to gain insights. Although there have been recent reviews in this field, there is a remarkable gap that addresses four sides of the problem. Namely, the application of video surveillance in smart cities, algorithms, datasets, and embedded systems. In this paper, we discuss the latest datasets used, the algorithms used, and the recent advances in embedded systems to form edge vision computing are introduced. Moreover, future trends and challenges are addressed
Trends in European Climate Change Perception: Where the Effects of Climate Change go unnoticed
Climate change threatens global impacts in a variety of domains that must be limited by adaptation and mitigation measures. The successful implementation of such policies can strongly benefit from the general publicβs cooperation motivated by their own risk perceptions. Public participation can be promoted by tailoring policies to the populations they affect, which in turn results in the need for a deeper understanding of how different communities interact with the issue of climate change. Social media platforms such as the microblogging service Twitter have opened unprecedented opportunities for research on public perception in recent years, offering a continuous stream of user-generated data. Simultaneously, they represent a crucial discursive space in which members of the public develop and discuss their opinions and concerns about climate change. Subsequently, this thesis gains insight into the characteristics of public reactions to individual climate change effects and processes by investing corresponding corpora of tweets spanning a decade. For seven western European countries, the spatial, temporal, and thematic reaction patterns are determined with a further assessment of the drivers behind each finding. Tweets are collected, classified, georeferenced, and clustered using a selection of Geographic Information Retrieval as well as Natural Language Processing methods before being analysed regarding thematic trends in their content, spatial distributions
and influences of environmental factors, as well temporal distributions and impacts of real-world events. The findings illustrate diverse climate change perceptions that vary across spatial, temporal, and thematic dimensions. Communities tend to focus more on issues relevant to their local or national environment, leading populations to develop a certain degree of specialisation for these aspects of climate change. This typically coincides with a substantially more domestic discourse on the subject and a decrease in interest for corresponding international events. In a similar sense, the tangibility of an event drives the magnitude of reactions. However, while more tangible events are more frequently recognised and discussed, less tangible events tend to be more frequently attributed to climate change as the public shifts their focus from immediate impacts on the personal scale to impacts on the global scale. Additionally, traditional news media are shown to retain a high level of control over science communication and the climate change discourse on Twitter, likely influencing the publicβs perspective on global warming. Individual real-world events such as major climate conferences and scientific releases only occasionally elicit strong public reactions when they are topically related to an event type, whereas global protests can lead to significant discussion across various event types. Inversely, global crises such as the COVID-19 pandemic significantly reduce public concern about climate change processes
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