scholarly journals Integrating EfficientNet into an HAFNet Structure for Building Mapping in High-Resolution Optical Earth Observation Data

2021 ◽  
Vol 13 (21) ◽  
pp. 4361
Author(s):  
Luca Ferrari ◽  
Fabio Dell’Acqua ◽  
Peng Zhang ◽  
Peijun Du
Keyword(s):  
High Resolution ◽  
Earth Observation ◽  
Light Detection And Ranging ◽  
Observation Data ◽  
Building Extraction ◽  
Multiple Sources ◽  
Light Detection ◽  
Single Thread ◽  
Combining Data ◽  
Benchmark Datasets

Automated extraction of buildings from Earth observation (EO) data is important for various applications, including updating of maps, risk assessment, urban planning, and policy-making. Combining data from different sensors, such as high-resolution multispectral images (HRI) and light detection and ranging (LiDAR) data, has shown great potential in building extraction. Deep learning (DL) is increasingly used in multi-modal data fusion and urban object extraction. However, DL-based multi-modal fusion networks may under-perform due to insufficient learning of “joint features” from multiple sources and oversimplified approaches to fusing multi-modal features. Recently, a hybrid attention-aware fusion network (HAFNet) has been proposed for building extraction from a dataset, including co-located Very-High-Resolution (VHR) optical images and light detection and ranging (LiDAR) joint data. The system reported good performances thanks to the adaptivity of the attention mechanism to the features of the information content of the three streams but suffered from model over-parametrization, which inevitably leads to long training times and heavy computational load. In this paper, the authors propose a restructuring of the scheme, which involved replacing VGG-16-like encoders with the recently proposed EfficientNet, whose advantages counteract exactly the issues found with the HAFNet scheme. The novel configuration was tested on multiple benchmark datasets, reporting great improvements in terms of processing times, and also in terms of accuracy. The new scheme, called HAFNetE (HAFNet with EfficientNet integration), appears indeed capable of achieving good results with less parameters, translating into better computational efficiency. Based on these findings, we can conclude that, given the current advancements in single-thread schemes, the classical multi-thread HAFNet scheme could be effectively transformed by the HAFNetE scheme by replacing VGG-16 with EfficientNet blocks on each single thread. The remarkable reduction achieved in computational requirements moves the system one step closer to on-board implementation in a possible, future “urban mapping” satellite constellation.

scholarly journals 2nd Edition of Instrumenting Smart City Applications with Big Sensing and Earth Observatory Data: Tools, Methods and Techniques

2021 ◽  
Vol 13 (7) ◽  
pp. 1310
Author(s):  
Gabriele Bitelli ◽  
Emanuele Mandanici
Keyword(s):  
High Resolution ◽  
Exponential Growth ◽  
Smart City ◽  
Earth Observation ◽  
Urban Environments ◽  
Observation Data ◽  
High Resolution Imagery ◽  
Observatory Data ◽  
Methods And Techniques ◽  
Earth Observation Data

The exponential growth in the volume of Earth observation data and the increasing quality and availability of high-resolution imagery are increasingly making more applications possible in urban environments [...]

scholarly journals LiDAR and other evidence for the southwest continuation and Late Quaternary reactivation of the Norumbega Fault System and a cross-cutting structure near Biddeford, Maine, USA

2019 ◽  
Vol 55 ◽  
pp. 323-359
Author(s):  
Ronald T. Marple ◽  
James D. Hurd
Keyword(s):  
High Resolution ◽  
New Hampshire ◽  
Late Quaternary ◽  
Light Detection And Ranging ◽  
Fault System ◽  
Light Detection ◽  
Drainage Patterns ◽  
Geomorphic Features ◽  
Wide Zone

High-resolution LiDAR (light detection and ranging) images reveal numerous NE-SW-trending geomorphic lineaments that may represent the southwest continuation of the Norumbega fault system (NFS) along a broad, 30- to 50-km-wide zone of brittle faults that continues at least 100 km across southern Maine and southeastern New Hampshire. These lineaments are characterized by linear depressions and valleys, linear drainage patterns, abrupt bends in rivers, and linear scarps. The Nonesuch River, South Portland, and Mackworth faults of the NFS appear to continue up to 100 km southwest of the Saco River along prominent but discontinuous LiDAR lineaments. Southeast-facing scarps that cross drumlins along some of the lineaments in southern Maine suggest that late Quaternary displacements have occurred along these lineaments. Several NW-SE-trending geomorphic features and geophysical lineaments near Biddeford, Maine, may represent a 30-km-long, NW-SE-trending structure that crosses part of the NFS. Brittle NWSE-trending, pre-Triassic faults in the Kittery Formation at Biddeford Pool, Maine, support this hypothesis.

scholarly journals A Hybrid Attention-Aware Fusion Network (HAFNet) for Building Extraction from High-Resolution Imagery and LiDAR Data

2020 ◽  
Vol 12 (22) ◽  
pp. 3764
Author(s):  
Peng Zhang ◽  
Peijun Du ◽  
Cong Lin ◽  
Xin Wang ◽  
Erzhu Li ◽  
...  
Keyword(s):  
High Resolution ◽  
Multimodal Fusion ◽  
Surface Model ◽  
Lidar Data ◽  
Building Extraction ◽  
High Resolution Imagery ◽  
Combining Data ◽  
Multimodal Features ◽  
Multimodal Data Fusion ◽  
The Individual

Automated extraction of buildings from earth observation (EO) data has long been a fundamental but challenging research topic. Combining data from different modalities (e.g., high-resolution imagery (HRI) and light detection and ranging (LiDAR) data) has shown great potential in building extraction. Recent studies have examined the role that deep learning (DL) could play in both multimodal data fusion and urban object extraction. However, DL-based multimodal fusion networks may encounter the following limitations: (1) the individual modal and cross-modal features, which we consider both useful and important for final prediction, cannot be sufficiently learned and utilized and (2) the multimodal features are fused by a simple summation or concatenation, which appears ambiguous in selecting cross-modal complementary information. In this paper, we address these two limitations by proposing a hybrid attention-aware fusion network (HAFNet) for building extraction. It consists of RGB-specific, digital surface model (DSM)-specific, and cross-modal streams to sufficiently learn and utilize both individual modal and cross-modal features. Furthermore, an attention-aware multimodal fusion block (Att-MFBlock) was introduced to overcome the fusion problem by adaptively selecting and combining complementary features from each modality. Extensive experiments conducted on two publicly available datasets demonstrated the effectiveness of the proposed HAFNet for building extraction.

Mining High Resolution Earth Observation Data Cubes

2021 ◽  
Author(s):  
Andreas Zuefle ◽  
Konrad Wessels ◽  
Dieter Pfoser
Keyword(s):  
High Resolution ◽  
Earth Observation ◽  
Observation Data ◽  
Data Cubes ◽  
Earth Observation Data

scholarly journals ATTENTION BASED CONVOLUTIONAL NEURAL NETWORK FOR BUILDING EXTRACTION FROM VERY HIGH RESOLUTION REMOTE SENSING IMAGE

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 507-512
Author(s):  
H. R. Hosseinpoor ◽  
F. Samadzadegan
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Feature Learning ◽  
Remote Sensing Data ◽  
Superior Performance ◽  
Model Complexity ◽  
Building Extraction ◽  
Learning Networks ◽  
Benchmark Datasets ◽  
Very High

Abstract. Buildings are a major element in the formation of cities and are essential for urban mapping. The precise extraction of buildings from remote sensing data has become a significant topic and has received much attention in recent years. The recently developed convolutional neural networks have shown effective and superior performance to perform well on learning high-level and discriminative features in extracting buildings because of the outstanding feature learning and end-to-end pixel labelling abilities. However, it is difficult to use the features of different levels with a certain degree of importance that is appropriate to deep learning networks. To tackle this problem, a network based on U-Nets and the attention mechanism block was proposed. The network contains an encoder part and a decoder part and a spatial attention module. The special architecture presented in this article enhances the propagation of features and effectively utilizes the features at various levels to reduce errors. The other remarkable thing is that attention module blocks only lead to a minimal increase in model complexity. We effectively demonstrate an improvement of building extraction accuracy on challenging Potsdam and Vaihingen benchmark datasets. The results of this paper show that the proposed architecture improves building extraction in very high resolution remote sensing images compared to previous models.

Potential of Sentinel and high resolution EO data for monitoring nature protection in cities – the SeMoNa22 project

2021 ◽  
Author(s):  
Anna Iglseder ◽  
Markus Immitzer ◽  
Christoph Bauerhansl ◽  
Hannes Hoffert-Hösl ◽  
Klaus Kramer ◽  
...  
Keyword(s):  
Time Series ◽  
Nature Conservation ◽  
High Resolution ◽  
Satellite Data ◽  
Laser Scanning ◽  
Object Oriented ◽  
Earth Observation ◽  
Observation Data ◽  
Earth Observation Data ◽  
Sentinel 2

<p><span><span>At the end of the 1980s the Municipal Department for Environmental Protection of Vienna - MA 22 initiated a detailed biotope mapping on the basis of the Viennese nature conservation law. Approximately 40 % of Vienna’s city area were covered, however only 2 % of the densely populated areas. This biotope mapping was the basis for the biotope types mapping (2005-2011) and of </span></span><span><span>the</span></span><span><span> green areas monitoring (2005). An update of these surveys has been planned in order to meet the various requirements of urban nature conservation and the national and international, respectively, legal monitoring and reporting obligations.</span></span></p><p><span><span>Since the 1970s the municipality of Vienna has built up a comprehensive database and uses state-of-the-art methods for collecting geodata carrying out services for surveying, airborne imaging and laser-scanning. Currently systems for mobile mapping, oblique aerial photos and a surveying flight with a single photon LiDAR system are being implemented or prepared. Because of the numerous high-resolution data available within the municipality and limitations mainly in spatial resolution of satellite data, the City of Vienna saw no need or benefit in integrating satellite images until now.</span></span></p><p><span><span>However, satellite data are now available within the European Copernicus program, which have considerable potential for monitoring green spaces and biotope types due to their high temporal resolution and the large number of spectral channels and SAR data. For the first time, the Sentinel-1 mission offers a combination of high spatial resolution in Interferometric Wide Swath (IW) recording mode and high temporal coverage of up to four shots every 12 days in cross-polarization in the C-band. The Sentinel-2 satellites deliver multispectral data in 10 channels every 5 days with spatial resolutions of 10 or 20 m.</span></span></p><p><span><span>Within the SeMoNa22 project, various indicators are derived for the Vienna urban area (2015-2020) and used for object-oriented mapping and classification of biotope types and characterization of the green space:</span></span></p><ul><li> <p><span><span>Sentinel-1 data (→ time series on the annual cycles in the backscattering properties of the vegetation, phenology),</span></span></p> </li> <li> <p><span><span>Sentinel-2 data (→ multispectral time series via parameters for habitat classification / vegetation indices),</span></span></p> </li> <li> <p><span><span>High-resolution earth observation data (airborne laser scanning (ALS), image matching, orthophoto → various parameter describing the horizontal and vertical vegetation structure).</span></span></p> </li> </ul><p><span><span>The main goals of SeMoNa22 is to explore efficient and effective ways of knowing if, how and to what extent the data collected can form the basis and become an integrative part of urban conservation monitoring. For this purpose, combinations of different earth observation data (satellite- and aircraft- supported or terrestrial sensors) and existing structured fieldwork data collections (species mapping, soil parameters, meteorology) are examined by means of pixel- and object-oriented methods of remote sensing and image processing. The study is done for several test sites in Vienna covering different ecosystems. In this contribution the ongoing SeMoNa22 project will be presented and first results will be shown and discussed.</span></span></p>

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