Towards the large-scale extraction of historical land cover information from historical maps

With the increasing relevance of the Internet of Things and large-scale location-based services, LoRa localization has been attractive due to its low-cost, low-power, and long-range properties. However, existing localization approaches based on received signal strength indicators are either easily affected by signal fading of different land-cover types or labor intensive. In this work, we propose SateLoc, a LoRa localization system that utilizes satellite images to generate virtual fingerprints. Specifically, SateLoc first uses high-resolution satellite images to identify land-cover types. With the path loss parameters of each land-cover type, SateLoc can automatically generate a virtual fingerprinting map for each gateway. We then propose a novel multi-gateway combination strategy, which is weighted by the environmental interference of each gateway, to produce a joint likelihood distribution for localization and tracking. We implement SateLoc with commercial LoRa devices without any hardware modification, and evaluate its performance in a 227,500-m urban area. Experimental results show that SateLoc achieves a median localization error of 43.5 m, improving more than 50% compared to state-of-the-art model-based approaches. Moreover, SateLoc can achieve a median tracking error of 37.9 m with the distance constraint of adjacent estimated locations. More importantly, compared to fingerprinting-based approaches, SateLoc does not require the labor-intensive fingerprint acquisition process.

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Classification of Very-High-Spatial-Resolution Aerial Images Based on Multiscale Features with Limited Semantic Information

Remote Sensing ◽

10.3390/rs13030364 ◽

2021 ◽

Vol 13 (3) ◽

pp. 364

Author(s):

Han Gao ◽

Jinhui Guo ◽

Peng Guo ◽

Xiuwan Chen

Keyword(s):

Deep Learning ◽

Land Cover ◽

Spatial Resolution ◽

Large Scale ◽

High Spatial Resolution ◽

Training Data ◽

Aerial Images ◽

Rural Landscapes ◽

Feature Representations ◽

Object Based

Recently, deep learning has become the most innovative trend for a variety of high-spatial-resolution remote sensing imaging applications. However, large-scale land cover classification via traditional convolutional neural networks (CNNs) with sliding windows is computationally expensive and produces coarse results. Additionally, although such supervised learning approaches have performed well, collecting and annotating datasets for every task are extremely laborious, especially for those fully supervised cases where the pixel-level ground-truth labels are dense. In this work, we propose a new object-oriented deep learning framework that leverages residual networks with different depths to learn adjacent feature representations by embedding a multibranch architecture in the deep learning pipeline. The idea is to exploit limited training data at different neighboring scales to make a tradeoff between weak semantics and strong feature representations for operational land cover mapping tasks. We draw from established geographic object-based image analysis (GEOBIA) as an auxiliary module to reduce the computational burden of spatial reasoning and optimize the classification boundaries. We evaluated the proposed approach on two subdecimeter-resolution datasets involving both urban and rural landscapes. It presented better classification accuracy (88.9%) compared to traditional object-based deep learning methods and achieves an excellent inference time (11.3 s/ha).

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Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data

Remote Sensing ◽

10.3390/rs12010096 ◽

2019 ◽

Vol 12 (1) ◽

pp. 96 ◽

Cited By ~ 6

Author(s):

James Brinkhoff ◽

Justin Vardanega ◽

Andrew J. Robson

Keyword(s):

Land Cover ◽

Large Scale ◽

Satellite Image ◽

Resource Planning ◽

Support Vector ◽

Perennial Crop ◽

Perennial Crops ◽

Object Based ◽

Rbf Kernel

Land cover mapping of intensive cropping areas facilitates an enhanced regional response to biosecurity threats and to natural disasters such as drought and flooding. Such maps also provide information for natural resource planning and analysis of the temporal and spatial trends in crop distribution and gross production. In this work, 10 meter resolution land cover maps were generated over a 6200 km2 area of the Riverina region in New South Wales (NSW), Australia, with a focus on locating the most important perennial crops in the region. The maps discriminated between 12 classes, including nine perennial crop classes. A satellite image time series (SITS) of freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery was used. A segmentation technique grouped spectrally similar adjacent pixels together, to enable object-based image analysis (OBIA). K-means unsupervised clustering was used to filter training points and classify some map areas, which improved supervised classification of the remaining areas. The support vector machine (SVM) supervised classifier with radial basis function (RBF) kernel gave the best results among several algorithms trialled. The accuracies of maps generated using several combinations of the multispectral and radar bands were compared to assess the relative value of each combination. An object-based post classification refinement step was developed, enabling optimization of the tradeoff between producers’ accuracy and users’ accuracy. Accuracy was assessed against randomly sampled segments, and the final map achieved an overall count-based accuracy of 84.8% and area-weighted accuracy of 90.9%. Producers’ accuracies for the perennial crop classes ranged from 78 to 100%, and users’ accuracies ranged from 63 to 100%. This work develops methods to generate detailed and large-scale maps that accurately discriminate between many perennial crops and can be updated frequently.

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Domain-Adversarial Training of Self-Attention-Based Networks for Land Cover Classification Using Multi-Temporal Sentinel-2 Satellite Imagery

Remote Sensing ◽

10.3390/rs13132564 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2564

Author(s):

Mauro Martini ◽

Vittorio Mazzia ◽

Aleem Khaliq ◽

Marcello Chiaberge

Keyword(s):

Land Cover ◽

Large Scale ◽

Domain Adaptation ◽

Learning Approaches ◽

Atmospheric Conditions ◽

Practical Applications ◽

Area Of Interest ◽

Multi Temporal ◽

Significant Performance ◽

Adversarial Training

The increasing availability of large-scale remote sensing labeled data has prompted researchers to develop increasingly precise and accurate data-driven models for land cover and crop classification (LC&CC). Moreover, with the introduction of self-attention and introspection mechanisms, deep learning approaches have shown promising results in processing long temporal sequences in the multi-spectral domain with a contained computational request. Nevertheless, most practical applications cannot rely on labeled data, and in the field, surveys are a time-consuming solution that pose strict limitations to the number of collected samples. Moreover, atmospheric conditions and specific geographical region characteristics constitute a relevant domain gap that does not allow direct applicability of a trained model on the available dataset to the area of interest. In this paper, we investigate adversarial training of deep neural networks to bridge the domain discrepancy between distinct geographical zones. In particular, we perform a thorough analysis of domain adaptation applied to challenging multi-spectral, multi-temporal data, accurately highlighting the advantages of adapting state-of-the-art self-attention-based models for LC&CC to different target zones where labeled data are not available. Extensive experimentation demonstrated significant performance and generalization gain in applying domain-adversarial training to source and target regions with marked dissimilarities between the distribution of extracted features.

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Object-based image segmentation in photogrammetry for cartographic use

10.5194/egusphere-egu21-11843 ◽

2021 ◽

Author(s):

Béla Kovács ◽

Márton Pál ◽

Fanni Vörös

Keyword(s):

Land Cover ◽

Large Scale ◽

Autonomous Vehicle ◽

Unsupervised Classification ◽

Large Set ◽

Bare Rock ◽

Object Based ◽

Social Fund ◽

Elevation Data ◽

Control Technologies

The use of aerial photography in topography has started in the first decades of the 20th century. Remote sensed data have become indispensable for cartographers and GIS staff when doing large-scale mapping: especially topographic, orienteering and thematic maps. The use of UAVs (unmanned aerial vehicles) for this purpose has also become widespread for some years. Various drones and sensors (RGB, multispectral and hyperspectral) with many specifications are used to capture and process the physical properties of an examined area. In parallel with the development of the hardware, new software solutions are emerging to visualize and analyse photogrammetric material: a large set of algorithms with different approaches are available for image processing.Our study focuses on the large-scale topographic mapping of vegetation and land cover. Most traditional analogue and digital maps use these layers either for background or highlighted thematic purposes. We propose to use the theory of OBIA &#8211; Object-based Image Analysis to differentiate cover types. This method involves pixels to be grouped into larger polygon units based on either spectral or other variables (e.g. elevation, aspect, curvature in case of DEMs). The neighbours of initial seed points are examined whether they should be added to the region according to the similarity of their attributes. Using OBIA, different land cover types (trees, grass, soils, bare rock surfaces) can be distinguished either with supervised or unsupervised classification &#8211; depending on the purposes of the analyst. Our base data were high-resolution RGB and multispectral images (with 5 bands).Following this methodology, not only elevation data (e.g. shaded relief or vector contour lines) can be derived from UAV imagery but vector land cover data are available for cartographers and GIS analysts. As the number of distinct land cover groups is free to choose, even quite complex thematic layers can be produced. These layers can serve as subjects of further analyses or for cartographic visualization.&#160;BK is supported by Application Domain Specific Highly Reliable IT Solutions&#8221; project &#160;has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the Thematic Excellence Programme TKP2020-NKA-06 (National Challenges Subprogramme) funding scheme.MP and FV are supported by EFOP-3.6.3-VEKOP-16-2017-00001: Talent Management in Autonomous Vehicle Control Technologies &#8211; The Project is financed by the Hungarian Government and co-financed by the European Social Fund.

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Large-Scale Land Cover Classification in Gaofen-2 Satellite Imagery

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2018.8518389 ◽

2018 ◽

Cited By ~ 3

Author(s):

Xin-Yi Tong ◽

Qikai Lu ◽

Gui-Song Xia ◽

Liangpei Zhang

Keyword(s):

Land Cover ◽

Satellite Imagery ◽

Large Scale ◽

Land Cover Classification ◽

Large Scale Land

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Olomouc - Possibilities of Geovisualization of the Historical City

Geoinformatics FCE CTU ◽

10.14311/gi.6.33 ◽

2011 ◽

Vol 6 ◽

pp. 267-274

Author(s):

Stanislav Popelka ◽

Alžběta Brychtová

Keyword(s):

Spatial Data ◽

Large Scale ◽

18Th Century ◽

3D Visualization ◽

3D Models ◽

Google Earth ◽

Point Of View ◽

World Heritage Site ◽

Historical Maps ◽

The City

Olomouc, nowadays a city with 100,000 inhabitants, has always been considered as one of the most prominent Czech cities. It is a social and economical centre, which history started just about the 11th century. The present appearance of the city has its roots in the 18th century, when the city was almost razed to the ground after the Thirty years’ war and a great fire in 1709. After that, the city was rebuilt to a baroque military fortress against Prussia army. At the beginning of the 20th century the majority of the fortress was demolished. Character of the town is dominated by the large number of churches, burgher’s houses and other architecturally significant buildings, like a Holy Trinity Column, a UNESCO World Heritage Site. Aim of this project was to state the most suitable methods of visualization of spatial-temporal change in historical build-up area from the tourist’s point of view, and to design and evaluate possibilities of spatial data acquisition. There are many methods of 2D and 3D visualization which are suitable for depiction of historical and contemporary situation. In the article four approaches are discussed comparison of historical and recent pictures or photos, overlaying historical maps over the orthophoto, enhanced visualization of historical map in large scale using the third dimension and photorealistic 3D models of the same area in different ages. All mentioned methods were geolocalizated using the Google Earth environment and multimedia features were added to enhance the impression of perception. Possibilities of visualization, which were outlined above, were realized on a case study of the Olomouc city. As a source of historical data were used rapport plans of the bastion fortress from the 17th century. The accuracy of historical maps was confirmed by cartometric methods with use of the MapAnalyst software. Registration of the spatial-temporal changes information has a great potential in urban planning or realization of reconstruction and particularly in the propagation of the region and increasing the knowledge of citizens about the history of Olomouc.

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