A Decentralized Federated Learning Paradigm for Semantic Segmentation of Geospatial Data

2022 ◽  
pp. 196-206
Author(s):  
Yash Khasgiwala ◽  
Dion Trevor Castellino ◽  
Sujata Deshmukh
Keyword(s):  
Semantic Segmentation ◽  
Geospatial Data ◽  
Learning Paradigm

scholarly journals Mapping the Essential Urban Land Use in Changchun by Applying Random Forest and Multi-Source Geospatial Data

2020 ◽  
Vol 12 (15) ◽  
pp. 2488 ◽  
Author(s):  
Shouzhi Chang ◽  
Zongming Wang ◽  
Dehua Mao ◽  
Kehan Guan ◽  
Mingming Jia ◽  
...  
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Urban Space ◽  
Semantic Segmentation ◽  
Urban Land ◽  
Urban Land Use ◽  
Geospatial Data ◽  
Classification Model ◽  
Rapid Acquisition ◽  
Street View

Understanding urban spatial pattern of land use is of great significance to urban land management and resource allocation. Urban space has strong heterogeneity, and thus there were many researches focusing on the identification of urban land use. The emergence of multiple new types of geospatial data provide an opportunity to investigate the methods of mapping essential urban land use. The popularization of street view images represented by Baidu Maps is benificial to the rapid acquisition of high-precision street view data, which has attracted the attention of scholars in the field of urban research. In this study, OpenStreetMap (OSM) was used to delineate parcels which were recognized as basic mapping units. A semantic segmentation of street view images was combined to enrich the multi-dimensional description of urban parcels, together with point of interest (POI), Sentinel-2A, and Luojia-1 nighttime light data. Furthermore, random forest (RF) was applied to determine the urban land use categories. The results show that street view elements are related to urban land use in the perspective of spatial distribution. It is reasonable and feasible to describe urban parcels according to the characteristics of street view elements. Due to the participation of street view, the overall accuracy reaches 79.13%. The contribution of street view features to the optimal classification model reached 20.6%, which is more stable than POI features.

scholarly journals QUANTIFYING UNCERTAINTY IN CLASSIFIED POINT CLOUD DATA FOR GEOSPATIAL APPLICATIONS

2020 ◽  
Vol XLIV-M-2-2020 ◽  
pp. 87-93
Author(s):  
S. Sen ◽  
N. Turel
Keyword(s):  
Point Cloud ◽  
Semantic Segmentation ◽  
Geospatial Data ◽  
Training Data ◽  
Point Cloud Data ◽  
Positional Accuracy ◽  
Data Infrastructure ◽  
Cloud Data ◽  
Digital Twins ◽  
Quantifying Uncertainty

Abstract. Classified Point Cloud data are increasingly the form of geospatial data that are used in engineering applications, smart digital twins and geospatial data infrastructure around the globe. Characterized by high positional accuracy such dense 3D datasets are often rated very highly for accuracy and reliability. However such data pose important challenges in semantic segmentation, especially in the context of Machine Learning(ML) techniques and the training data employed to provide classification codes to every point in massive point cloud datasets. These challenges are particularly significant since ML based processing of data is almost unavoidable due to the massive nature of the data that. We review different sources of uncertainty introduced by ML based classification and segmentation and outline concepts of uncertainty that is inherent in such automatically processed data. We also provide a theoretical framework for quantification of such uncertainty and argue that the standards of accuracy of such data should account for errors and omissions during auto segmentation and classification in addition to positional accuracy measures. Interestingly, the ability to quantify accuracies of ML based automation for processing such data is limited by the volume and velocity of such data.

scholarly journals SEMANTIC SEGMENTATION AND UNREGISTERED BUILDING DETECTION FROM UAV IMAGES USING A DECONVOLUTIONAL NETWORK

2018 ◽  
Vol XLII-2 ◽  
pp. 419-424
Author(s):  
S. Ham ◽  
Y. Oh ◽  
K. Choi ◽  
I. Lee
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Semantic Segmentation ◽  
Geospatial Data ◽  
Aerial Images ◽  
Building Detection ◽  
Urban Management ◽  
Probability Map ◽  
Gis Data ◽  
Uav Images ◽  
Uav Image

Detecting unregistered buildings from aerial images is an important task for urban management such as inspection of illegal buildings in green belt or update of GIS database. Moreover, the data acquisition platform of photogrammetry is evolving from manned aircraft to UAVs (Unmanned Aerial Vehicles). However, it is very costly and time-consuming to detect unregistered buildings from UAV images since the interpretation of aerial images still relies on manual efforts. To overcome this problem, we propose a system which automatically detects unregistered buildings from UAV images based on deep learning methods. Specifically, we train a deconvolutional network with publicly opened geospatial data, semantically segment a given UAV image into a building probability map and compare the building map with existing GIS data. Through this procedure, we could detect unregistered buildings from UAV images automatically and efficiently. We expect that the proposed system can be applied for various urban management tasks such as monitoring illegal buildings or illegal land-use change.

The Effect of Outcome Probability on Generalization in Predictive Learning

2019 ◽  
Vol 66 (1) ◽  
pp. 23-39 ◽  
Author(s):  
Hadar Ram ◽  
Dieter Struyf ◽  
Bram Vervliet ◽  
Gal Menahem ◽  
Nira Liberman
Keyword(s):  
High Probability ◽  
Lower Probability ◽  
Perceptual Similarity ◽  
Learning Paradigm ◽  
Predictive Learning ◽  
Lightning Bolt ◽  
Outcome Probability

Abstract. People apply what they learn from experience not only to the experienced stimuli, but also to novel stimuli. But what determines how widely people generalize what they have learned? Using a predictive learning paradigm, we examined the hypothesis that a low (vs. high) probability of an outcome following a predicting stimulus would widen generalization. In three experiments, participants learned which stimulus predicted an outcome (S+) and which stimulus did not (S−) and then indicated how much they expected the outcome after each of eight novel stimuli ranging in perceptual similarity to S+ and S−. The stimuli were rings of different sizes and the outcome was a picture of a lightning bolt. As hypothesized, a lower probability of the outcome widened generalization. That is, novel stimuli that were similar to S+ (but not to S−) produced expectations for the outcome that were as high as those associated with S+.

Face Completion Using Semantic Segmentation and Geometric Features

2018 ◽  
Vol 11 (6) ◽  
pp. 304
Author(s):  
Javier Pinzon-Arenas ◽  
Robinson Jimenez-Moreno ◽  
Ruben Hernandez-Beleno
Keyword(s):  
Semantic Segmentation ◽  
Geometric Features

Web Based GIS Application using Open Source Software for Sharing Geospatial Data

2015 ◽  
Vol 4 (1) ◽  
pp. 1224-1228 ◽  
Author(s):  
Debasish Chakraborty ◽  
◽  
Debanjan Sarkar ◽  
Shubham Agarwal ◽  
Dibyendu Dutta ◽  
...  
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Geospatial Data ◽  
Web Based ◽  
Gis Application

Using the potential of peripheral green areas of cities for educational and recreational purposes on the example of Stary Sącz, Nowy Targ and Kraków

2020 ◽  
Vol 65 (1) ◽  
pp. 53-63
Author(s):  
Mateusz Kulig ◽  
Anna Przeniczny ◽  
Piotr Ogórek
Keyword(s):  
Land Cover ◽  
Environmental Protection ◽  
Public Spaces ◽  
Geospatial Data ◽  
Valuable Resource ◽  
Green Areas

AbstractGreen areas located on the peripheries of cities have the potential to become green public spaces not only of recreational but also educational character, promoting at the same time the knowledge about environmental protection. The cities included in the research belong to the małopolskie voivodeship (Lesser Poland voivodeship). With the use of geospatial data of land cover, as well as territorial forms of environmental protection, it was pointed that 48.4% of forest, wooded and shrub green areas located within city borders are covered by a form of environmental protection, thus being a valuable resource of significant nature potential. Making such spaces available in a conscious and attractive way is presented on the example of projects implemented in the cities of: Stary Sącz, Nowy Targ and Kraków. The presented projects were used to make recommendations for city authorities to create green public spaces.

Forest canopy disturbance geospatial data for Lake Superior basin, USA, 1985-2008

2014 ◽  
Author(s):  
Ian W. Housman ◽  
Mark D. Nelson ◽  
Charles H. Perry ◽  
Kirk M. Stueve ◽  
Chengquan Huang
Keyword(s):  
Forest Canopy ◽  
Lake Superior ◽  
Geospatial Data ◽  
Canopy Disturbance
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