24.4.7 DEM, Surface Analysis for Ridges and Streams, Rivers, Drainage, Depressions

Chapter Contents (Back)
Curvature Analysis. Ridge. Valley. Drainage. Rivers.
See also Gully, Erosion, Rivers.
See also Landform Analysis, Landform Description.
See also Curvature and Features of Surfaces and Range Data.
See also Crater Detection, Impact Craters, Depressions.
See also Hydrological Analysis, Hydrological Modeling.

Peucker, T.K.[Thomas K.], Douglas, D.H.[David H.],
Detection of Surface-Specific Points by Local Parallel Processing of Discrete Terrain Elevation Data,
CGIP(4), No. 4, 1975, pp. 375-387.
Elsevier DOI BibRef 7500

Johnston, E.G., Rosenfeld, A.,
Digital Detection of Pits, Peaks, Ridges, and Ravines,
SMC(5), July 1975, pp. 472-480. BibRef 7507

Haralick, R.M., Campbell, J.B., Wang, S.,
Automatic Inference of Elevation and Drainage Models from a Satellite Image,
PIEEE(73), 1985, pp. 1040-1053. Recognize Drainage Networks. BibRef 8500

Haralick, R.M., Wang, S., Shapiro, L.G., Campbell, J.B.,
Extraction of Drainage Networks by Using the Consistent Labeling Technique,
RSE(18), No. 10, October, 1985, pp. 163-175. BibRef 8510

Haralick, R.M., Wang, S., and Elliott, D.B.,
Spatial Reasoning to Determine Stream Network from LANDSAT Imagery,
ICPR82(502-514). This paper uses a lot of different other algorithms to do the basic processing but finally determines surface orientations which can give ridge and valley locations. The basic processing is to eliminate effects of known LANDSAT noise, use ratios of images to classify into any number of classes, use the classes to determine regions of the same object, use the intensity average for a class to get the average intensity, the rest is caused by the surface orientation differences. Darker and brighter regions are slopes, these then give valleys and ridge lines. Then compute approximate elevation data from the slopes. It produces interesting results.
See also Topographic Classification of Digital Image Intensity Surfaces Using Generalized Splines and the Discrete Cosine Transform. BibRef 8200

Wang, S., Haralick, R.M., and Campbell, J.B.,
Relative Elevation Determination from Landsat Imagery,
CVWS84(150-157). Has some of the same properties as the stream network paper above. Generate an approximation of the elevation based on the shadows, etc. BibRef 8400

Haralick, R.M., Zhang, M.C., and Campbell, J.B.,
Automatic Delineation of Drainage Basins Within Digital Elevation Data Using the Topographic Primal Sketch,
Mathematical Geology(22), No. 2, 1990, pp. 189-209. BibRef 9000

O'Callaghan, J.F.[John F.], Mark, D.M.[David M.],
The Extraction of Drainage Networks from Digital Elevation Data,
CVGIP(28), No. 3, December 1984, pp. 323-344.
Elsevier DOI Recognize Drainage Networks. Uses digital terrain data for input. BibRef 8412

Soille, P., Gratin, C.,
An Efficient Algorithm for Drainage Network Extraction on DEMs,
JVCIR(5), 1994, pp. 181-189. BibRef 9400

Seemuller, W.W.[William W.],
The Extraction of Ordered Vector Drainage Networks form Elevation Data,
CVGIP(47), No. 1, July 1989, pp. 45-58.
Elsevier DOI Convert raster data to vectors with gaps, structure the results to get the networks. BibRef 8907

Monga, O., and Benayoun, S.,
Using Partial Derivatives of 3D Images to Extract Typical Surface-Features,
CVIU(61), No. 2, March 1995, pp. 171-189.
DOI Link BibRef 9503
Earlier:
Using Differential Geometry in R4 to Extract Typical Features in 3D Images,
CVPR93(684-685).
IEEE DOI BibRef
Earlier:
Using Differential Geometry in R4 to Extract Typical Features in 3D Density Images,
ICPR92(I:379-382).
IEEE DOI BibRef

Monga, O., Benayoun, S., and Faugeras, O.D.,
From Partial Derivatives of 3D Density Images to Ridge Lines,
CVPR92(354-359).
IEEE DOI Curvature analysis of range images. BibRef 9200

Monga, O., Armande, N., Montesinos, P.,
Thin Nets and Crest Lines: Application to Satellite Data and Medical Images,
CVIU(67), No. 3, September 1997, pp. 285-295.
DOI Link 9710
BibRef
Earlier: ICIP95(II: 468-471).
IEEE DOI 9510

See also Thin Nets Extraction Using a Multi-scale Approach. BibRef

Fua, P.[Pascal],
Fast, Accurate and Consistent Modeling of Drainage and Surrounding Terrain,
IJCV(26), No. 3, March 1998, pp. 215-234.
DOI Link 9804
BibRef
Earlier:
Model-Based Approach to Accurate and Consistent 3-D Modeling of Drainage and Surrounding Terrain,
CVPR97(903-908).
IEEE DOI 9704
BibRef
And:
3-D Modeling of Drainage Patterns,
SRI-TN- No. 555, September 23, 1996.
See also Model-Based Optimization: An Approach to Fast, Accurate and Consistent Site Modeling. BibRef

Kweon, I.S., Kanade, T.,
Extracting Topographic Terrain Features from Elevation Maps,
CVGIP(59), No. 2, March 1994, pp. 171-182.
DOI Link BibRef 9403

Wladis, D.[David],
Automatic Lineament Detection Using Digital Elevation Models with Second Derivative Filters,
PhEngRS(65), No. 4, April 1999, pp. 453-458. BibRef 9904

Wilson, R.C.[Richard C.], Hancock, E.R.[Edwin R.],
Consistent topographic surface labelling,
PR(32), No. 7, July 1999, pp. 1211-1223.
Elsevier DOI BibRef 9907

Lopez, A.M., Lumbreras, F., Serrat, J., Villanueva, J.J.,
Evaluation of Methods for Ridge and Valley Detection,
PAMI(21), No. 4, April 1999, pp. 327-335.
IEEE DOI Primarily drainage patterns, but apply to other line (maximum) detection problems. Has several references to the general problem from the 1800s. BibRef 9904

Jones, K.H.[Kevin H.],
Comparison of Two Approaches to Ranking Algorithms Used to Compute Hill Slopes,
GeoInfo(2), No. 3, October 1998, pp. 235-256.
DOI Link BibRef 9810

Eberly, D., Gardner, R., Morse, B.S., Pizer, S.M., and Scharlach, C.,
Ridges for Image Analysis,
JMIV(4), 1994, pp. 353-373. Multiscale ridge detection. BibRef 9400

Sagar, B.S.D.[B. S. Daya], Venu, M., Srinivas, D.,
Morphological operators to extract channel networks from digital elevation models,
JRS(21), No. 1, January 2000, pp. 21. 9911
BibRef

Sagar, B.S.D.[B.S. Daya], Lim, S.L.[Sin Liang],
Ranks for Pairs of Spatial Fields via Metric Based on Grayscale Morphological Distances,
IP(24), No. 3, March 2015, pp. 908-918.
IEEE DOI 1502
image classification BibRef

Baudemont, F., Parrot, J.F.,
Structural Analysis of DEM's by Intersection of Surface Normals in a Three-Dimensional Accumulator Space,
GeoRS(38), No. 3, May 2000, pp. 1191-1198.
IEEE Top Reference. 0006
BibRef

Cronin, T.M.[Terrence M.],
Classifying Hills and Valleys in Digitized Terrain,
PhEngRS(66), No. 9, September 2000, pp. 1129-1138. Use the elevation-based containment properties of a contour map. 0010
BibRef

Miliaresis, G.C., Argialas, D.P.,
Extraction and Delineation of Alluvial Fans from Digital Elevation Models and Landsat Thematic Mapper Images,
PhEngRS(66), No. 9, September 2000, pp. 1093-1102. A region-growing segmentation algorithm. 0010
BibRef

Thibault, D.[David], Gold, C.M.[Christopher M.],
Terrain Reconstruction from Contours by Skeleton Construction,
GeoInfo(4), No. 4, December 2000, pp. 349-373.
DOI Link 0101
BibRef

Kenward, T.[Tracey], Lettenmaier, D.P.[Dennis P.], Wood, E.F.[Eric F.], Fielding, E.[Eric],
Effects of Digital Elevation Model Accuracy on Hydrologic Predictions,
RSE(74), No. 3, 2000, pp. 432- 444. 0102
BibRef

Ahmadzadeh, M.R., Petrou, M.,
Error statistics for slope and aspect when derived from interpolated data,
GeoRS(39), No. 9, September 2001, pp. 1823-1833.
IEEE Top Reference. 0111
BibRef

Bittner, T.[Thomas], Stell, J.G.[John G.],
Vagueness and Rough Location,
GeoInfo(6), No. 2, June 2002, pp. 99-121.
DOI Link Description of location of vague GIS objects (i.e. a valley). BibRef 0206

Bittner, T.[Thomas],
Vague distance predicates,
GeoInfo(21), No. 2, April 2017, pp. 209-229.
WWW Link. 1702
BibRef

Mizukoshi, H.[Hiroko], Aniya, M.[Masamu],
Use of Contour-Based DEMs for Deriving and Mapping Topographic Attributes,
PhEngRS(68), No. 1, January 2002, pp. 83-93. The methods to calculate slope gradient and aspect and to classify slope form using contour-based DEMs are presented.
WWW Link. 0201
BibRef

Dillabaugh, C.R.[Craig R.], Niemann, K.O.[K. Olaf], Richardson, D.E.[Dianne E.],
Semi-Automated Extraction of Rivers from Digital Imagery,
GeoInfo(6), No. 3, September 2002, pp. 263-284.
DOI Link 0208
BibRef

Yokoyama, R.[Ryuzo], Shirasawa, M.[Michio], Pike, R.J.[Richard J.],
Visualizing Topography by Openness: A New Application of Image Processing to Digital Elevation Models,
PhEngRS(68), No. 3, March 2002, pp. 257-266. Maps of positive and negative openness, computed at a DEM point from zenith or nadir angles constrained by neighboring points, aid topographic interpretation by emphasizing ridges, drainages, and convexities and concavities.
WWW Link. 0204
BibRef

Arge, L.[Lars], Chase, J.S.[Jeffrey S.], Halpin, P.[Patrick], Toma, L.[Laura], Vitter, J.S.[Jeffrey S.], Urban, D.[Dean], Wickremesinghe, R.[Rajiv],
Efficient Flow Computation on Massive Grid Terrain Datasets,
GeoInfo(7), No. 4, December 2003, pp. 283-313.
DOI Link 0309
BibRef

Clarke, S.[Sharon], Burnett, K.[Kelly],
Comparison of Digital Elevation Models for Aquatic Data Development,
PhEngRS(69), No. 12, December 2003, pp. 1367-1376. Slopes, streams, and hydrologic units generated from 10-meter drainage-enforced digital elevation models and 30-meter digital elevation models are compared.
WWW Link. 0401
BibRef

Kim, K.M.[Kyoung Min], Park, J.J.[Joong Jo], Song, M.H.[Myung Hyun], Kim, I.C.[In Cheol], Suen, C.Y.[Ching Y.],
Detection of ridges and ravines using fuzzy logic operations,
PRL(25), No. 6, 19 April 2004, pp. 743-751.
Elsevier DOI 0405
BibRef

Aguilar, F.J.[Fernando J.], Agüera, F.[Francisco], Aguilar, M.A.[Manuel A.], Carvajal, F.[Fernando],
Effects of Terrain Morphology, Sampling Density, and Interpolation Methods on Grid DEM Accuracy,
PhEngRS(71), No. 7, July 2005, pp. 805-816. Effects of terrain morphology, sampling density, and scattered data interpolation methods on the accuracy of interpolated Grid Elevation Models.
WWW Link. 0509
BibRef

Baker, M.E.[Matthew E.], Weller, D.E.[Donald E.], Jordan, T.E.[Thomas E.],
Comparison of Automated Watershed Delineations: Effects on Land Cover Areas, Percentages, and Relationships to Nutrient Discharge,
PhEngRS(72), No. 2, February 2006, pp. 159-168.
WWW Link. A comparison of manual and automated watershed delineation procedures and their effects on a multi-regional analysis of land-cover and stream nutrient concentration. 0602
BibRef

Lindsay, J.B.[John B.], Creed, I.F.[Irena F.],
Sensitivity of Digital Landscapes to Artifact Depressions in Remotely-Sensed DEMs,
PhEngRS(71), No. 9, September 2005, pp. 1029-1036.
WWW Link. 0602
The effectiveness of inadequate representation of surfaces by grids and random errors in elevations to cause artifact depressions in digital elevation models of a range of landscape types is quantified. BibRef

Hofer, M., Sapiro, G., Wallner, J.,
Fair Polyline Networks for Constrained Smoothing of Digital Terrain Elevation Data,
GeoRS(44), No. 10, October 2006, pp. 2983-2990.
IEEE DOI 0609
BibRef

Silván-Cárdenas, J.L., Wang, L.,
A multi-resolution approach for filtering LiDAR altimetry data,
PandRS(61), No. 1, October 2006, pp. 11-22.
Elsevier DOI 0610
Scale-space representation; Multiscale Hermite transform BibRef

Shi, X.[Xun], Zhu, A.X.[A-Xing], Burt, J.[James], Choi, W.[Wes], Wang, R.X.[Rong-Xun], Pei, T.[Tao], Li, B.L.[Bao-Lin], Qin, C.Z.[Cheng-Zhi],
An Experiment Using a Circular Neighborhood to Calculate Slope Gradient from a DEM,
PhEngRS(73), No. 2, February 2007, pp. 143-154.
WWW Link. 0704
Research comparing the slope gradient values calculated using a circular neighborhood with the results from the traditional square neighborhood used in a focal operation on a raster layer. BibRef

Garrigues, S., Allard, D., Baret, F.,
Using First- and Second-Order Variograms for Characterizing Landscape Spatial Structures From Remote Sensing Imagery,
GeoRS(45), No. 6, June 2007, pp. 1823-1834.
IEEE DOI 0706
BibRef

Sathyamoorthy, D.[Dinesh],
The effect of morphological smoothening by reconstruction on the extraction of peaks and pits from digital elevation models,
PRL(28), No. 12, 1 September 2007, pp. 1400-1406.
Elsevier DOI 0707
Digital elevation models; Morphological smoothening by reconstruction; Kernel; Peaks and pits; DEM smoothening BibRef

Ai, T.H.[Ting-Hua],
The drainage network extraction from contour lines for contour line generalization,
PandRS(62), No. 2, June 2007, pp. 93-103.
Elsevier DOI 0709
Map generalization; Delaunay triangulation; Valley extraction; Contour generalization BibRef

Bóo, M., Amor, M., Döllner, J.,
Unified Hybrid Terrain Representation Based on Local Convexifications,
GeoInfo(11), No. 3, September 2007, pp. 331-357.
Springer DOI 0709
BibRef

Liu, J.K.[Jung-Kung], Li, R.X.[Rong-Xing], Deshpande, S.[Sagar], Niu, X.T.[Xu-Tong], Shih, T.Y.[Tian-Yuan],
Estimation of Blufflines Using Topographic Lidar Data and Orthoimages,
PhEngRS(75), No. 1, January 2009, pp. 69-80.
WWW Link. 0902
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Song, Y.H.[Yong-Hak], Shan, J.[Jie],
An Adaptive Approach to Topographic Feature Extraction from Digital Terrain Models,
PhEngRS(75), No. 3, March 2009, pp. 281-290.
WWW Link. 0903
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Chatterjee, R.S., Saha, S.K., Kumar, S.[Suresh], Mathew, S.[Sharika], Lakhera, R.C., Dadhwal, V.K.,
Interferometric SAR for characterization of ravines as a function of their density, depth, and surface cover,
PandRS(64), No. 5, September 2009, pp. 472-481.
Elsevier DOI 0910
Ravines; InSAR; Characterization; Chambal; India BibRef

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Co-registration and correlation of aerial photographs for ground deformation measurements,
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Elsevier DOI 1001
Aerial; Photography; Change detection; Registration; Correlation BibRef

Ai, T.H.[Ting-Hua], Li, J.Z.[Jing-Zhong],
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DEM; Map generalization; Terrain representation; Multi-scale representation BibRef

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LiDAR; Detection; Gullies; Airborne laser scanning; Morphology BibRef

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Zhang, H.H.[Hui-Hui], Loáiciga, H.A.[Hugo A.], Feng, L.W.[Lu-Wei], He, J.[Jing], Du, Q.Y.[Qing-Yun],
Setting the Flow Accumulation Threshold Based on Environmental and Morphologic Features to Extract River Networks from Digital Elevation Models,
IJGI(10), No. 3, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Hu, S.M.[Shi-Min], Fong, S.[Simon], Yang, L.L.[Li-Li], Yang, S.H.[Shuang-Hua], Dey, N.[Nilanjan], Millham, R.C.[Richard C.], Fiaidhi, J.[Jinan],
Fast and Accurate Terrain Image Classification for ASTER Remote Sensing by Data Stream Mining and Evolutionary-EAC Instance-Learning-Based Algorithm,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Babic, M.[Matej], Petrovic, D.[Dušan], Sodnik, J.[Jošt], Soldo, B.[Božo], Komac, M.[Marko], Chernieva, O.[Olena], Kovacic, M.[Miha], Mikoš, M.[Matjaž], Calì, M.[Michele],
Modeling and Classification of Alluvial Fans with DEMs and Machine Learning Methods: A Case Study of Slovenian Torrential Fans,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Zhang, W.[Wei], Li, W.K.[Wen-Kai], Loaiciga, H.A.[Hugo A.], Liu, X.G.[Xiu-Guo], Liu, S.Y.[Shu-Ya], Zheng, S.J.[Sheng-Jie], Zhang, H.[Han],
Adaptive Determination of the Flow Accumulation Threshold for Extracting Drainage Networks from DEMs,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106
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Wang, H.[Hang], Huang, G.T.[Guang-Tan], Chen, Y.K.[Yang-Kang],
Robust Nonstationary Local Slope Estimation,
GeoRS(59), No. 7, July 2021, pp. 6225-6233.
IEEE DOI 2106
Smoothing methods, Estimation, Transforms, Inverse problems, Mathematical model, Optimization, Reliability, Local slope, structural filtering BibRef

Telbisz, T.[Tamás],
Lidar-Based Morphometry of Conical Hills in Temperate Karst Areas in Slovenia,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
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Liu, C.Y.[Cheng-Yi], Wu, F.[Fang], Gong, X.Y.[Xian-Yong], Xing, R.X.[Rui-Xing], Du, J.W.[Jia-Wei],
Pattern Recognition of Complex Distributed Ditches,
IJGI(10), No. 7, 2021, pp. xx-yy.
DOI Link 2108
Agricultural drainage system. BibRef

Wei, Z.H.[Zhi-Hao], Jia, K.B.[Ke-Bin], Liu, P.Y.[Peng-Yu], Jia, X.W.[Xiao-Wei], Xie, Y.Q.[Yi-Qun], Jiang, Z.[Zhe],
Large-Scale River Mapping Using Contrastive Learning and Multi-Source Satellite Imagery,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Grau, J.[Joan], Liang, K.[Kang], Ogilvie, J.[Jae], Arp, P.[Paul], Li, S.[Sheng], Robertson, B.[Bonnie], Meng, F.R.[Fan-Rui],
Using Unmanned Aerial Vehicle and LiDAR-Derived DEMs to Estimate Channels of Small Tributary Streams,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Zhu, L.L.[Lin-Lin], Geng, X.[Xun], Li, Z.[Zheng], Liu, C.[Chun],
Improving YOLOv5 with Attention Mechanism for Detecting Boulders from Planetary Images,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Hansen, S.S.[Signe Schilling], Ernstsen, V.B.[Verner Brandbyge], Andersen, M.S.[Mikkel Skovgaard], Al-Hamdani, Z.[Zyad], Baran, R.[Ramona], Niederwieser, M.[Manfred], Steinbacher, F.[Frank], Kroon, A.[Aart],
Classification of Boulders in Coastal Environments Using Random Forest Machine Learning on Topo-Bathymetric LiDAR Data,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Yang, N.[Nai], Jiang, L.[Le], Chao, Y.[Yi], Li, Y.[Yang], Liu, P.C.[Peng-Cheng],
Influence of Relief Degree of Land Surface on Street Network Complexity in China,
IJGI(10), No. 10, 2021, pp. xx-yy.
DOI Link 2110
BibRef
And: Correction: IJGI(11), No. 1, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Uciechowska-Grakowicz, A.[Anna], Herrera-Granados, O.[Oscar],
Riverbed Mapping with the Usage of Deterministic and Geo-Statistical Interpolation Methods: The Odra River Case Study,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Pu, G.[Ge], Quackenbush, L.J.[Lindi J.], Stehman, S.V.[Stephen V.],
Identifying Factors That Influence Accuracy of Riparian Vegetation Classification and River Channel Delineation Mapped Using 1 m Data,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Barlow, M.C.[Mary C.], Zhu, X.X.[Xin-Xiang], Glennie, C.L.[Craig L.],
Stream Boundary Detection of a Hyper-Arid, Polar Region Using a U-Net Architecture: Taylor Valley, Antarctica,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Ferreira, Z.A.[Zuleide Alves], Cabral, P.[Pedro],
A Comparative Study about Vertical Accuracy of Four Freely Available Digital Elevation Models: A Case Study in the Balsas River Watershed, Brazil,
IJGI(11), No. 2, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Jancewicz, K.[Kacper], Rózycka, M.[Milena], Szymanowski, M.[Mariusz], Kryza, M.[Maciej], Migon, P.[Piotr],
Topographic Characteristics of Drainage Divides at the Mountain-Range Scale: A Review of DTM-Based Analytical Tools,
IJGI(11), No. 2, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Okolie, C.J.[Chukwuma J.], Smit, J.L.[Julian L.],
A systematic review and meta-analysis of Digital elevation model (DEM) fusion: pre-processing, methods and applications,
PandRS(188), 2022, pp. 1-29.
Elsevier DOI 2205
Data fusion, Remote sensing image fusion, Multi-sensor fusion, Digital elevation model fusion, InSAR, LiDAR, Weight maps BibRef

Xue, Y.[Yuan], Qin, C.[Chao], Wu, B.[Baosheng], Li, D.[Dan], Fu, X.D.[Xu-Dong],
Automatic Extraction of Mountain River Surface and Width Based on Multisource High-Resolution Satellite Images,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Huang, H.[Hai], Liu, J.J.[Jian-Jun], Wang, X.[Xing], Chen, Y.[Yuan], Zhang, Q.[Qing], Liu, D.W.[Da-Wei], Yan, W.[Wei], Ren, X.[Xin],
The Analysis of Cones within the Tianwen-1 Landing Area,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Hu, J.L.[Jin-Long], Luo, M.L.[Ming-Liang], Bai, L.C.[Lei-Chao], Duan, J.L.[Jin-Liang], Yu, B.[Bing],
An Integrated Algorithm for Extracting Terrain Feature-Point Clusters Based on DEM Data,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Farmakis-Serebryakova, M.[Marianna], Heitzler, M.[Magnus], Hurni, L.[Lorenz],
Terrain Segmentation Using a U-Net for Improved Relief Shading,
IJGI(11), No. 7, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Deng, B.[Bin], Xiong, K.[Kai], Huang, Z.Y.[Zhi-Yong], Jiang, C.B.[Chang-Bo], Liu, J.[Jiang], Luo, W.[Wei], Xiang, Y.F.[Yi-Fei],
Monitoring and Predicting Channel Morphology of the Tongtian River, Headwater of the Yangtze River Using Landsat Images and Lightweight Neural Network,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Yang, Z.K.[Zhong-Kang], Wei, J.B.[Jin-Bing], Deng, J.H.[Jian-Hui], Zhao, S.Y.[Si-Yuan],
An Improved Method for the Evaluation and Local Multi-Scale Optimization of the Automatic Extraction of Slope Units in Complex Terrains,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Qin, Z.L.[Zi-Long], Wang, J.X.[Jin-Xin],
Multifractal Correlation between Terrain and River Network Structure in the Yellow River Basin, China,
IJGI(11), No. 10, 2022, pp. xx-yy.
DOI Link 2211
BibRef

Shen, Y.G.[Yan-Gen], Li, A.[Anbo], Xu, S.Y.[Shi-Yu], Xie, X.L.[Xian-Li],
A Morphing-Based Method for Paleotopographic Reconstruction of the Transverse Canyon,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Zhang, W.B., Wu, C.Y., Bao, Z.S.,
SA-BiSeNet: Swap attention bilateral segmentation network for real-time inland waterways segmentation,
IET-IPR(17), No. 1, 2023, pp. 166-177.
DOI Link 2301
BibRef

Yuan, S.Q.[Shao-Qing], Fan, W.[Wen], Jiang, C.C.[Cheng-Cheng],
The Profiles Based on Ridge and Valley Lines to Extract Shoulder Lines on the Loess Plateau,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Sakashita, A.[Akimasa], Endo, N.[Noritaka],
Mobility and Location of Drainage Divides Affected by Tilting Uplift in Sado Island, Japan,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
BibRef

Lu, L.J.[Li-Jie], Wang, L.H.[Li-Hui], Yang, Q.[Qichi], Zhao, P.C.[Peng-Cheng], Du, Y.[Yun], Xiao, F.[Fei], Ling, F.[Feng],
Extracting a Connected River Network from DEM by Incorporating Surface River Occurrence Data and Sentinel-2 Imagery in the Danjiangkou Reservoir Area,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
BibRef

He, A.[Aoyang], Huang, J.C.[Jiang-Cheng], Sun, Z.B.[Zheng-Bao], Zhou, J.Y.[Jing-Yi], Yang, C.[Cheng],
Spatial and Temporal Evolution Characteristics of the Salween River Delta from 1973 to 2021,
RS(15), No. 5, 2023, pp. xx-yy.
DOI Link 2303
BibRef

Koski, C.[Christian], Kettunen, P.[Pyry], Poutanen, J.[Justus], Zhu, L.[Lingli], Oksanen, J.[Juha],
Mapping Small Watercourses from DEMs with Deep Learning: Exploring the Causes of False Predictions,
RS(15), No. 11, 2023, pp. 2776.
DOI Link 2306
BibRef

Xu, X.F.[Xiao-Feng], Liu, P.C.[Peng-Cheng], Guo, M.W.[Ming-Wu],
Drainage Pattern Recognition of River Network Based on Graph Convolutional Neural Network,
IJGI(12), No. 7, 2023, pp. xx-yy.
DOI Link 2308
BibRef

Dui, Z.X.[Zi-Xuan], Huang, Y.J.[Yong-Jian], Wang, M.Q.[Ming-Quan], Jin, J.P.[Jiu-Ping], Gu, Q.R.[Qian-Rong],
Segmentation and Connectivity Reconstruction of Urban Rivers from Sentinel-2 Multi-Spectral Imagery by the WaterSCNet Deep Learning Model,
RS(15), No. 19, 2023, pp. 4875.
DOI Link 2310
BibRef

Zhao, M.W.[Ming-Wei], Ju, X.X.[Xiao-Xiao], Wang, N.[Ni], Wang, C.[Chun], Zeng, W.[Weibo], Xu, Y.[Yan],
Parallel Channel Identification and Elimination Method Based on the Spatial Position Relationship of Different Channels,
IJGI(13), No. 1, 2024, pp. 13.
DOI Link 2402
From DEM data. BibRef

Zhang, J.[Jun], Zhang, J.[Jun], Huang, X.[Xiao], Zhou, W.[Weixun], Fu, H.Y.[Hu-Yan], Chen, Y.Y.[Yu-Yan], Zhan, Z.H.[Zheng-Hao],
Dual-Task Network for Terrace and Ridge Extraction: Automatic Terrace Extraction via Multi-Task Learning,
RS(16), No. 3, 2024, pp. 568.
DOI Link 2402
BibRef

Zhao, Y.H.[Ying-Hai], Zou, J.W.[Jia-Wei], Liu, S.H.[Su-Hong], Xie, Y.[Yun],
Terrace Extraction Method Based on Remote Sensing and a Novel Deep Learning Framework,
RS(16), No. 9, 2024, pp. 1649.
DOI Link 2405
BibRef

Lee, T.[Taesam], Hwang, S.[Seonghyeon], Singh, V.P.[Vijay P.],
KNN Local Linear Regression for Demarcating River Cross-Sections with Point Cloud Data from UAV Photogrammetry URiver-X,
RS(16), No. 10, 2024, pp. 1820.
DOI Link 2405
BibRef

Wernette, P.A.[Phillipe Alan],
Machine Learning Vegetation Filtering of Coastal Cliff and Bluff Point Clouds,
RS(16), No. 12, 2024, pp. 2169.
DOI Link 2406
BibRef

Wang, N.[Ning], Chen, F.[Fang], Yu, B.[Bo], Zhang, H.Y.[Hai-Ying], Zhao, H.[Huichen], Wang, L.[Lei],
Delineation of Intermittent Rivers and Ephemeral Streams Using a Hybrid Method,
RS(16), No. 13, 2024, pp. 2489.
DOI Link 2407
BibRef

Feng, H.[Haoan], Xu, X.[Xin], de Floriani, L.[Leila],
ImplicitTerrain: a Continuous Surface Model for Terrain Data Analysis,
NERF24(899-909)
IEEE DOI 2410
Analytical models, Accuracy, Data analysis, Computational modeling, Pipelines, Surface fitting, Data models, Terrain Data Analysis BibRef

Roncella, R., Forlani, G., Diotri, F.,
A Monte Carlo Simulation Study on the Dome Effect,
ISPRS21(B2-2021: 53-60).
DOI Link 2201
Dome shapes affect surface reconstruction. BibRef