Unmanned Aerial Systems for Wildland and Forest Fires

Wildfires represent an important natural risk causing economic losses, human death and important environmental damage. In recent years, we witness an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting. Systems were proposed for the remote detection and tracking of fires. These systems have shown improvements in the area of efficient data collection and fire characterization within small scale environments. However, wildfires cover large areas making some of the proposed ground-based systems unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial Systems (UAS) were proposed. UAS have proven to be useful due to their maneuverability, allowing for the implementation of remote sensing, allocation strategies and task planning. They can provide a low-cost alternative for the prevention, detection and real-time support of firefighting. In this paper we review previous work related to the use of UAS in wildfires. Onboard sensor instruments, fire perception algorithms and coordination strategies are considered. In addition, we present some of the recent frameworks proposing the use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at: https://doi.org/10.3390/drones501001

Learnable Earth Parser: Discovering 3D Prototypes in Aerial Scans

We propose an unsupervised method for parsing large 3D scans of real-world scenes into interpretable parts. Our goal is to provide a practical tool for analyzing 3D scenes with unique characteristics in the context of aerial surveying and mapping, without relying on application-specific user annotations. Our approach is based on a probabilistic reconstruction model that decomposes an input 3D point cloud into a small set of learned prototypical shapes. Our model provides an interpretable reconstruction of complex scenes and leads to relevant instance and semantic segmentations. To demonstrate the usefulness of our results, we introduce a novel dataset of seven diverse aerial LiDAR scans. We show that our method outperforms state-of-the-art unsupervised methods in terms of decomposition accuracy while remaining visually interpretable. Our method offers significant advantage over existing approaches, as it does not require any manual annotations, making it a practical and efficient tool for 3D scene analysis. Our code and dataset are available at https://imagine.enpc.fr/~loiseaur/learnable-earth-parse

Advances in Remote Sensing and GIS applications in Forest Fire Management: from local to global assessments

This report contains the proceedings of the 8th International Workshop of the European Association of Remote Sensing Laboratories (EARSeL) Special Interest Group on Forest Fires, that took place in Stresa, (Italy) on 20-21 October 2011. The main subject of the workshop was the operational use of remote sensing in forest fire management and different spatial scales were addressed, from local to regional and from national to global. Topics of the workshops were also grouped according to the fire management stage considered for the application of remote sensing techniques, addressing pre fire, during fire or post fire conditions.JRC.H.7-Land management and natural hazard

Benchmarking of airborne laser scanning based feature extraction methods and mobile laser scanning system performance based on high-quality test fields

Osajulkaisut: Publication 1: Kaartinen, H., Hyyppä, J., Gülch, E., Vosselman, G., Hyyppä, H., Matikainen, L., Hofmann, A.D., Mäder, U., Persson, Å., Söderman, U., Elmqvist, M., Ruiz, A., Dragoja, M., Flamanc, D., Maillet, G., Kersten, T., Carl, J., Hau, R., Wild, E., Frederiksen, L., Holmgaard, J. and Vester, K., 2005. Accuracy of 3D city models: EuroSDR comparison. Proceedings of ISPRS Workshop "Laser scanning 2005", September 12-14, 2005, Enschede, The Netherlands, The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVI(Part 3/W19), 227-232, CD-ROM. Publication 2: Kaartinen, H. and Hyyppä, J., 2006. EuroSDR-Project Commission 3 "Evaluation of Building Extraction", Final Report, In: EuroSDR - European Spatial Data Research, Official Publication No 50, 9-77. Publication 3: Kaartinen, H. and Hyyppä, J., 2008. EuroSDR/ISPRS Project, Commission II "Tree Extraction", Final Report, EuroSDR – European Spatial Data Research, Official Publication No 53, 56 p. Publication 4: Kaartinen, H., Hyyppä, J., Yu, X., Vastaranta, M., Hyyppä, H., Kukko, A., Holopainen, M., Heipke, C., Hirschmugl, M., Morsdorf, F., Næsset, E., Pitkänen, J., Popescu, S., Solberg, S., Wolf, B.M. and Wu, J.-C., 2012. An International Comparison of Individual Tree Detection and Extraction Using Airborne Laser Scanning. Remote Sensing, 4(4), 950-974. Publication 5: Kaartinen, H., Hyyppä, J., Kukko, A., Jaakkola, A. and Hyyppä, H., 2012. Benchmarking the Performance of Mobile Laser Scanning Systems Using a Permanent Test Field. Sensors 12(9), 12814-12835. Publication 6: Kaartinen, H., Hyyppä, J., Kukko, A., Lehtomäki, M., Jaakkola, A., Hyyppä, H., Vosselman,G., Elberink, S.O., Rutzinger; M., Pu, S. and Vaaja, M., 2013. EuroSDR-Project Commission II "Mobile Mapping - Road Environment Mapping using Mobile Laser Scanning", Final Report, In: EuroSDR - European Spatial Data Research, Official Publication No 62, 49-95.Comparing different feature extraction methods based on remote sensing or remote sensing systems is difficult as there are but few common data sets or test fields with reference data of high standard available for analysis. State-of-the-art methods and systems are often in still evolving stage and can be run only by the developers themselves. Establishing a high-quality test field is laborious, but once such a test field has been established, it becomes easier to set up the systems to collect data from the field than to collect reference data from new areas. Comparing either different systems or the same system with different parameters is easier when the number of variables is kept to a minimum; the remotely sensed areas are kept constant and any changes in them can be controlled more easily. The benchmarking results provide valuable information to both developers and users of remote sensing data products. The benchmarked feature extraction methods studied included extraction of buildings and individual trees using data from common test fields. The performance of the mobile laser scanning systems was benchmarked using data collected from an established urban test field. In all cases, it was concluded that the primary factor affecting the results was the method or the system, and this enabled a high degree of comparability for the results of the given extraction or mapping tasks.Erilaisten kaukokartoitukseen perustuvien kohdemallinnusmenetelmien tai kaukokartoitusjärjestelmien vertailu on vaikeaa koska yhteisesti käytettävissä olevia aineistoja tai testikenttiä, joista on saatavissa korkealaatuista referenssiaineistoa, on olemassa vain vähän. Uusimmat menetelmät ja järjestelmät ovat usein vielä kehitysvaiheessa ja niiden käyttö onnistuu vain niiden kehittäjiltä. Korkealaatuisen testikentän tekeminen on työlästä, mutta kun testikenttä on perustettu, on helpompaa kerätä aineistoja siltä eri järjestelmillä kuin mitata referenssiaineistoa uusilta alueilta. Eri järjestelmien tai yhden järjestelmän eri asetuksien vertailu on helpompaa kun muuttujien määrä on mahdollisimman pieni; tässä tapauksessa kaukokartoitetut alueet pysyvät vakiona ja mahdolliset muutokset niissä ovat helpommin kontrolloitavissa. Vertailujen tulokset antavat hyödyllistä tietoa sekä kaukokartoitustuotteiden kehittäjille että niiden käyttäjille. Vertaillut kohdemallinnusmenetelmät olivat rakennusten ja yksittäisten puiden mallinnus yhteisiltä testikentiltä kerättyjä aineistoja käyttäen. Liikkuvien laserkeilausjärjestelmien suorituskykyä vertailtiin käyttäen perustetulta kaupunkitestikentältä kerättyjä aineistoja. Kaikissa tapauksissa todettiin että tärkein tuloksiin vaikuttava tekijä oli menetelmä tai järjestelmä itse, joten annetun mallinnus- tai kartoitustehtävän tulokset ovat hyvin vertailukelpoisia

Automatic Fire Detection Using Computer Vision Techniques for UAV-based Forest Fire Surveillance

Due to their rapid response capability and maneuverability, extended operational range, and improved personnel safety, unmanned aerial vehicles (UAVs) with vision-based systems have great potentials for forest fire surveillance and detection. Over the last decade, it has shown an increasingly strong demand for UAV-based forest fire detection systems, as they can avoid many drawbacks of other forest fire detection systems based on satellites, manned aerial vehicles, and ground equipments. Despite this, the existing UAV-based forest fire detection systems still possess numerous practical issues for their use in operational conditions. In particular, the successful forest fire detection remains difficult, given highly complicated and non-structured environments of forest, smoke blocking the fire, motion of cameras mounted on UAVs, and analogues of flame characteristics. These adverse effects can seriously cause either false alarms or alarm failures. In order to successfully execute missions and meet their corresponding performance criteria and overcome these ever-increasing challenges, investigations on how to reduce false alarm rates, increase the probability of successful detection, and enhance adaptive capabilities to various circumstances are strongly demanded to improve the reliability and accuracy of forest fire detection system. According to the above-mentioned requirements, this thesis concentrates on the development of reliable and accurate forest fire detection algorithms which are applicable to UAVs. These algorithms provide a number of contributions, which include: (1) a two-layered forest fire detection method is designed considering both color and motion features of fire; it is expected to greatly improve the forest fire detection performance, while significantly reduce the motion of background caused by the movement of UAV; (2) a forest fire detection scheme is devised combining both visual and infrared images for increasing the accuracy and reliability of forest fire alarms; and (3) a learning-based fire detection approach is developed for distinguishing smoke (which is widely considered as an early signal of fire) from other analogues and achieving early stage fire detection

Efficient deep CNN-based fire detection and localization in video surveillance applications

Convolutional neural networks (CNNs) have yielded state-of-the-art performance in image classification and other computer vision tasks. Their application in fire detection systems will substantially improve detection accuracy, which will eventually minimize fire disasters and reduce the ecological and social ramifications. However, the major concern with CNN-based fire detection systems is their implementation in real-world surveillance networks, due to their high memory and computational requirements for inference. In this paper, we propose an original, energy-friendly, and computationally efficient CNN architecture, inspired by the SqueezeNet architecture for fire detection, localization, and semantic understanding of the scene of the fire. It uses smaller convolutional kernels and contains no dense, fully connected layers, which helps keep the computational requirements to a minimum. Despite its low computational needs, the experimental results demonstrate that our proposed solution achieves accuracies that are comparable to other, more complex models, mainly due to its increased depth. Moreover, this paper shows how a tradeoff can be reached between fire detection accuracy and efficiency, by considering the specific characteristics of the problem of interest and the variety of fire data

Cognitive privacy middleware for deep learning mashup in environmental IoT

Data mashup is a Web technology that combines information from multiple sources into a single Web application. Mashup applications support new services, such as environmental monitoring. The different organizations utilize data mashup services to merge data sets from the different Internet of Multimedia Things (IoMT) context-based services in order to leverage the performance of their data analytics. However, mashup, different data sets from multiple sources, is a privacy hazard as it might reveal citizens specific behaviors in different regions. In this paper, we present our efforts to build a cognitive-based middleware for private data mashup (CMPM) to serve a centralized environmental monitoring service. The proposed middleware is equipped with concealment mechanisms to preserve the privacy of the merged data sets from multiple IoMT networks involved in the mashup application. In addition, we presented an IoT-enabled data mashup service, where the multimedia data are collected from the various IoMT platforms, and then fed into an environmental deep learning service in order to detect interesting patterns in hazardous areas. The viable features within each region were extracted using a multiresolution wavelet transform, and then fed into a discriminative classifier to extract various patterns. We also provide a scenario for IoMT-enabled data mashup service and experimentation results

Comparison of object and pixel-based classifications for land-use and land cover mapping in the mountainous Mokhotlong District of Lesotho using high spatial resolution imagery

Research Report submitted in partial fulfilment for the degree of Master of Science (Geographical Information Systems and Remote Sensing) School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg. August 2016.The thematic classification of land use and land cover (LULC) from remotely sensed imagery data is one of the most common research branches of applied remote sensing sciences. The performances of the pixel-based image analysis (PBIA) and object-based image analysis (OBIA) Support Vector Machine (SVM) learning algorithms were subjected to comparative assessment using WorldView-2 and SPOT-6 multispectral images of the Mokhotlong District in Lesotho covering approximately an area of 100 km2. For this purpose, four LULC classification models were developed using the combination of SVM –based image analysis approach (i.e. OBIA and/or PBIA) on high resolution images (WorldView-2 and/or SPOT-6) and the results were subjected to comparisons with one another. Of the four LULC models, the OBIA and WorldView-2 model (overall accuracy 93.2%) was found to be more appropriate and reliable for remote sensing application purposes in this environment. The OBIA-WorldView-2 LULC model was subjected to spatial overlay analysis with DEM derived topographic variables in order to evaluate the relationship between the spatial distribution of LULC types and topography, particularly for topographically-controlled patterns. It was discovered that although that there are traces of the relationship between the LULC types distributions and topography, it was significantly convoluted due to both natural and anthropogenic forces such that the topographic-induced patterns for most of the LULC types had been substantial disrupted.LG201