HYBRID ACQUISITION OF HIGH QUALITY TRAINING DATA FOR SEMANTIC SEGMENTATION OF 3D POINT CLOUDS USING CROWD-BASED ACTIVE LEARNING

Kölle, M.; Walter, V.; Schmohl, S.; Soergel, U.

doi:https://doi.org/10.5194/isprs-annals-V-2-2020-501-2020

EVALUATION OF CONSUMER-GRADE AND SURVEY-GRADE UAV-LIDAR

G. Mandlburger, M. Kölle, F. Pöppl, and M. Cramer

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W3-2023, 99–106, https://doi.org/10.5194/isprs-archives-XLVIII-1-W3-2023-99-2023,https://doi.org/10.5194/isprs-archives-XLVIII-1-W3-2023-99-2023, 2023

A TWO-STAGE APPROACH FOR RARE CLASS SEGMENTATION IN LARGE-SCALE URBAN POINT CLOUDS

X. Zhang, R. Xue, and U. Soergel

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 329–334, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-329-2022,https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-329-2022, 2022

EVALUATION OF THE QUALITY OF REAL-TIME MAPPING WITH CRANE CAMERAS AND VISUAL SLAM ALGORITHMS

L. Joachim, W. Zhang, N. Haala, and U. Soergel

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 545–552, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-545-2022,https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-545-2022, 2022

URBAN CLASSIFICATION BASED ON TOP-VIEW POINT CLOUD AND SAR IMAGE FUSION WITH SWIN TRANSFORMER

R. Xue, X. Zhang, and U. Soergel

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 559–564, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-559-2022,https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-559-2022, 2022

A GAMIFICATION APPROACH FOR THE IMPROVEMENT OF PAID CROWD-BASED LABELLING OF GEOSPATIAL DATA

V. Walter, M. Kölle, and D. Collmar

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2022, 113–120, https://doi.org/10.5194/isprs-annals-V-4-2022-113-2022,https://doi.org/10.5194/isprs-annals-V-4-2022-113-2022, 2022

LEARNING FROM THE PAST: CROWD-DRIVEN ACTIVE TRANSFER LEARNING FOR SEMANTIC SEGMENTATION OF MULTI-TEMPORAL 3D POINT CLOUDS

M. Kölle, V. Walter, and U. Soergel

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 259–266, https://doi.org/10.5194/isprs-annals-V-2-2022-259-2022,https://doi.org/10.5194/isprs-annals-V-2-2022-259-2022, 2022

MATCHING PERSISTENT SCATTERER CLUSTERS TO BUILDING ELEMENTS IN MESH REPRESENTATION

P. J. Schneider and U. Soergel

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 123–130, https://doi.org/10.5194/isprs-annals-V-3-2022-123-2022,https://doi.org/10.5194/isprs-annals-V-3-2022-123-2022, 2022

WHICH 3D DATA REPRESENTATION DOES THE CROWD LIKE BEST? CROWD-BASED ACTIVE LEARNING FOR COUPLED SEMANTIC SEGMENTATION OF POINT CLOUDS AND TEXTURED MESHES

M. Kölle, D. Laupheimer, V. Walter, N. Haala, and U. Soergel

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2021, 93–100, https://doi.org/10.5194/isprs-annals-V-2-2021-93-2021,https://doi.org/10.5194/isprs-annals-V-2-2021-93-2021, 2021

SEGMENTATION OF BUILDINGS BASED ON HIGH RESOLUTION PERSISTENT SCATTERER POINT CLOUDS

P. J. Schneider and U. Soergel

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 65–71, https://doi.org/10.5194/isprs-annals-V-3-2021-65-2021,https://doi.org/10.5194/isprs-annals-V-3-2021-65-2021, 2021

A TWO-LEVEL APPROACH FOR THE CROWD-BASED COLLECTION OF VEHICLES FROM 3D POINT CLOUDS

V. Walter, M. Kölle, D. Collmar, and Y. Zhang

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2021, 97–104, https://doi.org/10.5194/isprs-annals-V-4-2021-97-2021,https://doi.org/10.5194/isprs-annals-V-4-2021-97-2021, 2021

HYBRID GEOREFERENCING, ENHANCEMENT AND CLASSIFICATION OF ULTRA-HIGH RESOLUTION UAV LIDAR AND IMAGE POINT CLOUDS FOR MONITORING APPLICATIONS

N. Haala, M. Kölle, M. Cramer, D. Laupheimer, G. Mandlburger, and P. Glira

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 727–734, https://doi.org/10.5194/isprs-annals-V-2-2020-727-2020,https://doi.org/10.5194/isprs-annals-V-2-2020-727-2020, 2020

IDENTIFICATION OF PEACH TREE TRUNKS FROM LASER SCANNING DATA OBTAINED WITH SMALL UNMANNED AERIAL SYSTEM

E. Hadas, M. Kölle, M. Karpina, and A. Borkowski

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 735–740, https://doi.org/10.5194/isprs-annals-V-2-2020-735-2020,https://doi.org/10.5194/isprs-annals-V-2-2020-735-2020, 2020

EXTRACTING AND EVALUATING CLUSTERS IN DINSAR DEFORMATION DATA ON SINGLE BUILDINGS

P. J. Schneider, R. Khamis, and U. Soergel

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 157–163, https://doi.org/10.5194/isprs-annals-V-3-2020-157-2020,https://doi.org/10.5194/isprs-annals-V-3-2020-157-2020, 2020

EVALUATION AND OPTIMISATION OF CROWD-BASED COLLECTION OF TREES FROM 3D POINT CLOUDS

V. Walter, M. Kölle, and Y. Yin

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2020, 49–56, https://doi.org/10.5194/isprs-annals-V-4-2020-49-2020,https://doi.org/10.5194/isprs-annals-V-4-2020-49-2020, 2020

SINGLE-IMAGE DEHAZING ON AERIAL IMAGERY USING CONVOLUTIONAL NEURAL NETWORKS

M. Madadikhaljan, R. Bahmanyar, S. M. Azimi, P. Reinartz, and U. Sörgel

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4-W18, 687–692, https://doi.org/10.5194/isprs-archives-XLII-4-W18-687-2019,https://doi.org/10.5194/isprs-archives-XLII-4-W18-687-2019, 2019

WORKABLE MONITORING SYSTEM BASED ON SPACEBORNE SAR IMAGES FOR MINING AREAS - STINGS DEVELOPMENT PROJECT

C. H. Yang, A. Müterthies, and U. Soergel

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1951–1957, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1951-2019,https://doi.org/10.5194/isprs-archives-XLII-2-W13-1951-2019, 2019

EVALUATION OF A PSI-BASED CHANGE DETECTION REGARDING SIMULATION, COMPARISON, AND APPLICATION

C. H. Yang and U. Soergel

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1959–1965, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1959-2019,https://doi.org/10.5194/isprs-archives-XLII-2-W13-1959-2019, 2019

SUBMANIFOLD SPARSE CONVOLUTIONAL NETWORKS FOR SEMANTIC SEGMENTATION OF LARGE-SCALE ALS POINT CLOUDS

S. Schmohl and U. Sörgel

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 77–84, https://doi.org/10.5194/isprs-annals-IV-2-W5-77-2019,https://doi.org/10.5194/isprs-annals-IV-2-W5-77-2019, 2019

INTEGRATION OF MULTIPLE COLLECTED POLYGONS WITH A RASTER-BASED APPROACH

V. Walter

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 679–685, https://doi.org/10.5194/isprs-archives-XLII-4-679-2018,https://doi.org/10.5194/isprs-archives-XLII-4-679-2018, 2018

ADAPTIVE 4D PSI-BASED CHANGE DETECTION

Chia-Hsiang Yang and Uwe Soergel

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3, 245–250, https://doi.org/10.5194/isprs-annals-IV-3-245-2018,https://doi.org/10.5194/isprs-annals-IV-3-245-2018, 2018

WATER SURFACE RECONSTRUCTION IN AIRBORNE LASER BATHYMETRY FROM REDUNDANT BED OBSERVATIONS

G. Mandlburger, N. Pfeifer, and U. Soergel

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W4, 123–130, https://doi.org/10.5194/isprs-annals-IV-2-W4-123-2017,https://doi.org/10.5194/isprs-annals-IV-2-W4-123-2017, 2017

Monitoring of Building Construction by 4D Change Detection Using Multi-temporal SAR Images

C. H. Yang, Y. Pang, and U. Soergel

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1-W1, 35–42, https://doi.org/10.5194/isprs-annals-IV-1-W1-35-2017,https://doi.org/10.5194/isprs-annals-IV-1-W1-35-2017, 2017

USE AND OPTIMISATION OF PAID CROWDSOURCING FOR THE COLLECTION OF GEODATA

V. Walter, D. Laupheimer, and D. Fritsch

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B4, 253–257, https://doi.org/10.5194/isprs-archives-XLI-B4-253-2016,https://doi.org/10.5194/isprs-archives-XLI-B4-253-2016, 2016

NETWORK DETECTION IN RASTER DATA USING MARKED POINT PROCESSES

A. Schmidt, C. Kruse, F. Rottensteiner, U. Soergel, and C. Heipke

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 701–708, https://doi.org/10.5194/isprs-archives-XLI-B3-701-2016,https://doi.org/10.5194/isprs-archives-XLI-B3-701-2016, 2016

HYBRID ACQUISITION OF HIGH QUALITY TRAINING DATA FOR SEMANTIC SEGMENTATION OF 3D POINT CLOUDS USING CROWD-BASED ACTIVE LEARNING

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