Calendar queues: a fast 0(1) priority queue implementation for the simulation event set problem
Communications of the ACM
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discrete-event simulation and the event horizon
PADS '94 Proceedings of the eighth workshop on Parallel and distributed simulation
Topographic distance and watershed lines
Signal Processing - Special issue on mathematical morphology and its applications to signal processing
Discrete-event simulation and the event horizon part 2: event list management
PADS '96 Proceedings of the tenth workshop on Parallel and distributed simulation
A comparative study of parallel and sequential priority queue algorithms
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Lazy queue: an efficient implementation of the pending-event set
ANSS '91 Proceedings of the 24th annual symposium on Simulation
Proceedings of the 32nd conference on Winter simulation
Dynamic Lazy Calendar Queue: An Event List for Network Simulation
HPC-ASIA '97 Proceedings of the High-Performance Computing on the Information Superhighway, HPC-Asia '97
SS '99 Proceedings of the Thirty-Second Annual Simulation Symposium
Efficient Flow Computation on Massive Grid Terrain Datasets
Geoinformatica
Ladder queue: An O(1) priority queue structure for large-scale discrete event simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Short note: O(N) implementation of the fast marching algorithm
Journal of Computational Physics
Sluggish Calendar Queues for Network Simulation
MASCOTS '06 Proceedings of the 14th IEEE International Symposium on Modeling, Analysis, and Simulation
Revisiting priority queues for image analysis
Pattern Recognition
Heuristic Search: Theory and Applications
Heuristic Search: Theory and Applications
Computing the drainage network on huge grid terrains
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
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Depressions (or pits) are areas within a digital elevation model that are surrounded by higher terrain, with no outlet to lower areas. Filling them so they are level, as fluid would fill them if the terrain was impermeable, is often necessary in preprocessing DEMs. The depression-filling algorithm presented here - called Priority-Flood - unifies and improves the work of a number of previous authors who have published similar algorithms. The algorithm operates by flooding DEMs inwards from their edges using a priority queue to determine the next cell to be flooded. The resultant DEM has no depressions or digital dams: every cell is guaranteed to drain. The algorithm is optimal for both integer and floating-point data, working in O(n) and O(nlog"2n) time, respectively. It is shown that by using a plain queue to fill depressions once they have been found, an O(mlog"2m) time-complexity can be achieved, where m does not exceed the number of cells n. This is the lowest time complexity of any known floating-point depression-filling algorithm. In testing, this improved variation of the algorithm performed up to 37% faster than the original. Additionally, a parallel version of an older, but widely used, depression-filling algorithm required six parallel processors to achieve a run-time on par with what the newer algorithm's improved variation took on a single processor. The Priority-Flood Algorithm is simple to understand and implement: the included pseudocode is only 20 lines and the included C++ reference implementation is under a hundred lines. The algorithm can work on irregular meshes as well as 4-, 6-, 8-, and n-connected grids. It can also be adapted to label watersheds and determine flow directions through either incremental elevation changes or depression carving. In the case of incremental elevation changes, the algorithm includes safety checks not present in prior works.