Iterative self-organizing SCEne-LEvel sampling (ISOSCELES) for large-scale building extraction

The explosion of data in the information age has provided an opportunity to explore the possibility of characterizing the climate patterns using data mining techniques. Nigeria has a unique tropical climate with two precipitation regimes: low precipitation in the north leading to aridity and desertification and high precipitation in parts of the southwest and southeast leading to large scale flooding. In this research, four indices have been used to characterize the intensity, frequency, and amount of rainfall over Nigeria. A type of Artificial Neural Network called the self-organizing map has been used to reduce the multiplicity of dimensions and produce four unique zones characterizing extreme precipitation conditions in Nigeria. This approach allowed for the assessment of spatial and temporal patterns in extreme precipitation in the last three decades. Precipitation properties in each cluster are discussed. The cluster closest to the Atlantic has high values of precipitation intensity, frequency, and duration, whereas the cluster closest to the Sahara Desert has low values. A significant increasing trend has been observed in the frequency of rainy days at the center of the northern region of Nigeria.

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Self-organizing Clustering: Non-hierarchical Clustering for Large Scale DNA Sequence Data

IPSJ Digital Courier ◽

10.2197/ipsjdc.3.193 ◽

2007 ◽

Vol 3 ◽

pp. 193-197 ◽

Cited By ~ 1

Author(s):

Kou Amano ◽

Hiroaki Ichikawa ◽

Hidemitsu Nakamura ◽

Hisataka Numa ◽

Kaoru Fukami-Kobayashi ◽

...

Keyword(s):

Dna Sequence ◽

Hierarchical Clustering ◽

Large Scale ◽

Sequence Data ◽

Dna Sequence Data ◽

Self Organizing

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Multiscale Semantic Feature Optimization and Fusion Network for Building Extraction Using High-Resolution Aerial Images and LiDAR Data

Remote Sensing ◽

10.3390/rs13132473 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2473

Author(s):

Qinglie Yuan ◽

Helmi Zulhaidi Mohd Shafri ◽

Aidi Hizami Alias ◽

Shaiful Jahari Hashim

Keyword(s):

High Resolution ◽

Large Scale ◽

Spatial Information ◽

Feature Fusion ◽

Aerial Images ◽

Semantic Gap ◽

Superior Performance ◽

Lidar Data ◽

Building Extraction ◽

Hierarchical Features

Automatic building extraction has been applied in many domains. It is also a challenging problem because of the complex scenes and multiscale. Deep learning algorithms, especially fully convolutional neural networks (FCNs), have shown robust feature extraction ability than traditional remote sensing data processing methods. However, hierarchical features from encoders with a fixed receptive field perform weak ability to obtain global semantic information. Local features in multiscale subregions cannot construct contextual interdependence and correlation, especially for large-scale building areas, which probably causes fragmentary extraction results due to intra-class feature variability. In addition, low-level features have accurate and fine-grained spatial information for tiny building structures but lack refinement and selection, and the semantic gap of across-level features is not conducive to feature fusion. To address the above problems, this paper proposes an FCN framework based on the residual network and provides the training pattern for multi-modal data combining the advantage of high-resolution aerial images and LiDAR data for building extraction. Two novel modules have been proposed for the optimization and integration of multiscale and across-level features. In particular, a multiscale context optimization module is designed to adaptively generate the feature representations for different subregions and effectively aggregate global context. A semantic guided spatial attention mechanism is introduced to refine shallow features and alleviate the semantic gap. Finally, hierarchical features are fused via the feature pyramid network. Compared with other state-of-the-art methods, experimental results demonstrate superior performance with 93.19 IoU, 97.56 OA on WHU datasets and 94.72 IoU, 97.84 OA on the Boston dataset, which shows that the proposed network can improve accuracy and achieve better performance for building extraction.

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Spark-GHSOM: Growing Hierarchical Self-Organizing Map for large scale mixed attribute datasets

Information Sciences ◽

10.1016/j.ins.2018.12.007 ◽

2019 ◽

Vol 496 ◽

pp. 572-591 ◽

Cited By ~ 5

Author(s):

Ameya Malondkar ◽

Roberto Corizzo ◽

Iluju Kiringa ◽

Michelangelo Ceci ◽

Nathalie Japkowicz

Keyword(s):

Large Scale ◽

Self Organizing Map ◽

Self Organizing

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Self Organizing Migrating Algorithm with Nelder Mead Crossover and Log-Logistic Mutation for Large Scale Optimization

Adaptation, Learning, and Optimization - Computational Intelligence for Big Data Analysis ◽

10.1007/978-3-319-16598-1_6 ◽

2015 ◽

pp. 143-164 ◽

Cited By ~ 2

Author(s):

Dipti Singh ◽

Seema Agrawal

Keyword(s):

Large Scale ◽

Large Scale Optimization ◽

Scale Optimization ◽

Self Organizing

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Using Self-Organizing Maps to Identify Coherent CONUS Precipitation Regions

Journal of Climate ◽

10.1175/jcli-d-19-0352.1 ◽

2019 ◽

Vol 32 (22) ◽

pp. 7747-7761 ◽

Cited By ~ 1

Author(s):

Leif M. Swenson ◽

Richard Grotjahn

Keyword(s):

Extreme Events ◽

Annual Cycle ◽

Large Scale ◽

Robust Statistics ◽

Grid Point ◽

Future Application ◽

Self Organizing Maps ◽

Annual Cycles ◽

Extreme Precipitation Events ◽

Self Organizing

Abstract Extreme precipitation events have major societal impacts. These events are rare and can have small spatial scale, making statistical analysis difficult; both factors are mitigated by combining events over a region. A methodology is presented to objectively define “coherent” regions wherein data points have matching annual cycles. Regions are found by training self-organizing maps (SOMs) on the annual cycle of precipitation for each grid point across the contiguous United States (CONUS). Using the annual cycle for our intended application minimizes problems caused by consecutive dry periods and localized extreme events. Multiple criteria are applied to identify useful numbers of regions for our future application. Criteria assess these properties for each region: having many more events than experienced by a single grid point, good connectedness and compactness, and robustness to changing the number of regions. Our methodology is applicable across datasets and is tested here on both reanalysis and gridded observational data. Precipitation regions obtained align with large-scale geographical features and are readily interpretable. Useful numbers of regions balance two conflicting preferences: larger regions contain more events and thereby have more robust statistics, but more compact regions allow weather patterns associated with extreme events to be aggregated with confidence. For 6-h precipitation, 12–15 regions over the CONUS optimize our metrics. The regions obtained are compared against two existing region archetypes. For example, a popular set of regions, based on nine groups of states, has less coherent regions than defining the same number of regions with our SOM methodology.

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The Role of Synoptic Cyclones for the Formation of Arctic Summer Circulation Patterns as Clustered by Self-Organizing Maps

Atmosphere ◽

10.3390/atmos10080474 ◽

2019 ◽

Vol 10 (8) ◽

pp. 474 ◽

Cited By ~ 1

Author(s):

Min-Hee Lee ◽

Joo-Hong Kim

Keyword(s):

Arctic Ocean ◽

Large Scale ◽

The Arctic ◽

Cyclone Activity ◽

Summer Circulation ◽

Self Organizing Maps ◽

Weather Patterns ◽

Atmospheric Circulation Patterns ◽

Circulation Patterns ◽

Self Organizing

Contribution of extra-tropical synoptic cyclones to the formation of mean summer atmospheric circulation patterns in the Arctic domain (≥60° N) was investigated by clustering dominant Arctic circulation patterns based on daily mean sea-level pressure using self-organizing maps (SOMs). Three SOM patterns were identified; one pattern had prevalent low-pressure anomalies in the Arctic Circle (SOM1), while two exhibited opposite dipoles with primary high-pressure anomalies covering the Arctic Ocean (SOM2 and SOM3). The time series of their occurrence frequencies demonstrated the largest inter-annual variation in SOM1, a slight decreasing trend in SOM2, and the abrupt upswing after 2007 in SOM3. Analyses of synoptic cyclone activity using the cyclone track data confirmed the vital contribution of synoptic cyclones to the formation of large-scale patterns. Arctic cyclone activity was enhanced in the SOM1, which was consistent with the meridional temperature gradient increases over the land–Arctic ocean boundaries co-located with major cyclone pathways. The composite daily synoptic evolution of each SOM revealed that all three SOMs persisted for less than five days on average. These evolutionary short-term weather patterns have substantial variability at inter-annual and longer timescales. Therefore, the synoptic-scale activity is central to forming the seasonal-mean climate of the Arctic.

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Super-Resolution-Based Snake Model—An Unsupervised Method for Large-Scale Building Extraction Using Airborne LiDAR Data and Optical Image

Remote Sensing ◽

10.3390/rs12111702 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1702 ◽

Cited By ~ 2

Author(s):

Thanh Huy Nguyen ◽

Sylvie Daniel ◽

Didier Guériot ◽

Christophe Sintès ◽

Jean-Marc Le Caillec

Keyword(s):

Large Scale ◽

Active Contour Model ◽

Super Resolution ◽

Airborne Lidar ◽

Lidar Data ◽

Force Model ◽

Building Extraction ◽

Snake Model ◽

Object Based

Automatic extraction of buildings in urban and residential scenes has become a subject of growing interest in the domain of photogrammetry and remote sensing, particularly since the mid-1990s. Active contour model, colloquially known as snake model, has been studied to extract buildings from aerial and satellite imagery. However, this task is still very challenging due to the complexity of building size, shape, and its surrounding environment. This complexity leads to a major obstacle for carrying out a reliable large-scale building extraction, since the involved prior information and assumptions on building such as shape, size, and color cannot be generalized over large areas. This paper presents an efficient snake model to overcome such a challenge, called Super-Resolution-based Snake Model (SRSM). The SRSM operates on high-resolution Light Detection and Ranging (LiDAR)-based elevation images—called z-images—generated by a super-resolution process applied to LiDAR data. The involved balloon force model is also improved to shrink or inflate adaptively, instead of inflating continuously. This method is applicable for a large scale such as city scale and even larger, while having a high level of automation and not requiring any prior knowledge nor training data from the urban scenes (hence unsupervised). It achieves high overall accuracy when tested on various datasets. For instance, the proposed SRSM yields an average area-based Quality of 86.57% and object-based Quality of 81.60% on the ISPRS Vaihingen benchmark datasets. Compared to other methods using this benchmark dataset, this level of accuracy is highly desirable even for a supervised method. Similarly desirable outcomes are obtained when carrying out the proposed SRSM on the whole City of Quebec (total area of 656 km2), yielding an area-based Quality of 62.37% and an object-based Quality of 63.21%.

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JointNet: A Common Neural Network for Road and Building Extraction

Remote Sensing ◽

10.3390/rs11060696 ◽

2019 ◽

Vol 11 (6) ◽

pp. 696 ◽

Cited By ~ 13

Author(s):

Zhengxin Zhang ◽

Yunhong Wang

Keyword(s):

Neural Network ◽

Receptive Field ◽

Loss Function ◽

Large Scale ◽

Connectivity Pattern ◽

Automatic Extraction ◽

Network Module ◽

Building Extraction ◽

Road Extraction ◽

General Method

Automatic extraction of ground objects is fundamental for many applications of remote sensing. It is valuable to extract different kinds of ground objects effectively by using a general method. We propose such a method, JointNet, which is a novel neural network to meet extraction requirements for both roads and buildings. The proposed method makes three contributions to road and building extraction: (1) in addition to the accurate extraction of small objects, it can extract large objects with a wide receptive field. By switching the loss function, the network can effectively extract multi-type ground objects, from road centerlines to large-scale buildings. (2) This network module combines the dense connectivity with the atrous convolution layers, maintaining the efficiency of the dense connection connectivity pattern and reaching a large receptive field. (3) The proposed method utilizes the focal loss function to improve road extraction. The proposed method is designed to be effective on both road and building extraction tasks. Experimental results on three datasets verified the effectiveness of JointNet in information extraction of road and building objects.

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