From Point Cloud to Grid DEM A Scalable Approach

From Point Cloud to Grid DEM:A Scalable Approach

Pankaj K.Agarwal 1,Lars Arge 12,and Andrew Danner 1

1Department of Computer Science,Duke University,Durham,NC27708,USA

{pankaj,large,adanner}@https://www.doczj.com/doc/7710606.html,

2Department of Computer Science,University of Aarhus,Aarhus,Denmark

large@daimi.au.dk

Summary.Given a set S of points in R3sampled from an elevation function H:R2→R, we present a scalable algorithm for constructing a grid digital elevation model(DEM).Our algorithm consists of three stages:First,we construct a quad tree on S to partition the point set into a set of non-overlapping segments.Next,for each segment q,we compute the set of points in q and all segments neighboring q.Finally,we interpolate each segment independently using points within the segment and its neighboring segments.

Data sets acquired by LIDAR and other modern mapping technologies consist of hundreds of millions of points and are too large to?t in main memory.When processing such massive data sets,the transfer of data between disk and main memory(also called I/O),rather than the CPU time,becomes the performance bottleneck.We therefore present an I/O-ef?cient algo-rithm for constructing a grid DEM.Our experiments show that the algorithm scales to data sets much larger than the size of main memory,while existing algorithms do not scale.For example,using a machine with1GB RAM,we were able to construct a grid DEM containing 1.3billion cells(occupying1.2GB)from a LIDAR data set of over390million points(occu-pying20GB)in about53hours.Neither ArcGIS nor GRASS,two popular GIS products,were able to process this data set.

1Introduction

One of the basic tasks of a geographic information system(GIS)is to store a repre-sentation of various physical properties of a terrain such as elevation,temperature, precipitation,or water depth,each of which can be viewed as a real-valued bivariate function.Because of simplicity and ef?cacy,one of the widely used representations is the so-called grid representation in which a functional value is stored in each cell of a two-dimensional uniform grid.However,many modern mapping technologies Supported by NSF under grants CCR-00-86013,EIA-01-31905,CCR-02-04118,and DEB-04-25465,by ARO grants W911NF-04-1-0278and DAAD19-03-1-0352,and by a grant from the U.S.–Israel Binational Science Foundation.

Supported in part by the US Army Research Of?ce through grant W911NF-04-1-0278and by an Ole R?mer Scholarship from the Danish National Science Research Council.

Supported by the US Army Research Of?ce through grant W911NF-04-1-0278.

2P.K.Agarwal,L.Arge,and A.Danner do no acquire data on a uniform grid.Hence the raw data is a set S of N(arbitrary) points in R3,sampled from a function H:R2→R.An important task in GIS is thus to interpolate S on a uniform grid of a prescribed resolution.

In this paper,we present a scalable algorithm for this interpolation problem.Al-though our technique is general,we focus on constructing a grid digital elevation model(DEM)from a set S of N points in R3acquired by modern mapping tech-niques such as LIDAR.These techniques generate huge amounts of high-resolution data.For example,LIDAR3acquires highly accurate elevation data at a resolution of one point per square meter or better and routinely generates hundreds of millions of points.It is not possible to store these massive data sets in the internal memory of even high-end machines,and the data must therefore reside on larger but consider-ably slower disks.When processing such huge data sets,the transfer of data between disk and main memory(also called I/O),rather than computation,becomes the per-formance bottleneck.An I/O-ef?cient algorithm that minimizes the number of disk accesses leads to tremendous runtime improvements in these cases.In this paper we develop an I/O-ef?cient algorithm for constructing a grid DEM of unprecedented size from massive LIDAR data sets.

Related work.A variety of methods for interpolating a surface from a set of points have been proposed,including inverse distance weighting(IDW),kriging, spline interpolation and minimum curvature surfaces.Refer to[12]and the refer-ences therein for a survey of the different methods.However,the computational complexity of these methods often make it infeasible to use them directly on even moderately large points sets.Therefore,many practical algorithms use a segmenta-tion scheme that decomposes the plane(or rather the area of the plane containing the input points)into a set of non-overlapping areas(or segments),each contain-ing a small number of input points.One then interpolates the points in each segment independently.Numerous segmentation schemes have been proposed,including sim-ple regular decompositions and decompositions based on V oronoi diagrams[18]or quad trees[14,11].A few schemes using overlapping segments have also been pro-posed[19,17].

As mentioned above,since I/O is typically the bottleneck when processing large data sets,I/O-ef?cient algorithms are designed to explicitly take advantage of the large main memory and disk block size[2].These algorithms are designed in a model in which the computer consists of an internal(or main)memory of size M and an in?nite external https://www.doczj.com/doc/7710606.html,putation is considered free but can only occur on ele-ments in main memory;in one I/O-operation,or simply I/O,B consecutive elements can be transfered between internal and external memory.The goal of an I/O-ef?cient algorithm is to minimize the number of I/Os.

ManyΘ(N)time algorithms that do not explicitly consider I/O useΘ(N)I/Os when used in the I/O-model.However,the“linear”bound,the number of I/Os needed to read N elements,is onlyΘ(scan(N))=Θ(N)in the I/O model.The number of 3In this paper,we consider LIDAR data sets that represent the actual terrain and have been pre-processed by the data providers to remove spikes and errors due to noise.

From Point Cloud to Grid DEM:A Scalable Approach 3

I/Os needed to sort N elements is Θ(sort(N ))=Θ(N B log M/B N B )[2].In prac-

tice,B is on the order of 103–105,so scan(N )and sort(N )are typically much smaller than N .Therefore tremendous speedups can often be obtained by develop-ing algorithms that use O (scan(N ))or O (sort(N ))I/Os rather than ?(N )I/Os.Numerous I/O-ef?cient algorithms and data structures have been developed in re-cent years,including several for fundamental GIS problems (refer to [4]and the references therein for a survey).Agarwal et.al [1]presented a general top-down lay-ered framework for constructing a certain class of spatial data structures–including quad trees–I/O-ef?ciently.Hjaltason and Samet [9]also presented an I/O-ef?cient quad-tree construction algorithm.This optimal O (sort(N ))I/O algorithm is based on assigning a Morton block index to each point in S ,encoding its location along a Morton-order (Z-order)space-?lling curve,sorting the points by this index,and then constructing the structure in a bottom-up manner.

Our approach.In this paper we describe an I/O-ef?cient algorithm for construct-ing a grid DEM from LIDAR points based on quad-tree segmentation.Most of the segmentation based algorithms for this problem can be considered as consisting of three separate phases;the segmentation phase,where the decomposition is computed based on S ;the neighbor ?nding phase,where for each segment in the decomposi-tion the points in the segment and the relevant neighboring segments are computed;and the interpolation phase,where a surface is interpolated in each segment and the interpolated values of the grid cells in the segment are computed.In this paper,we are more interested in the segmentation and neighbor ?nding phases than the par-ticular interpolation method used in the interpolation phase.We will focus on the quad tree based segmentation scheme because of its relative simplicity and because it has been used with several interpolation methods such as thin plate splines [14]and B-splines [11].We believe that our techniques will apply to other segmentation schemes as well.

Our algorithm implements all three phases I/O-ef?ciently,while allowing the use of any given interpolation method in the interpolation phase.Given a set S of N points,a desired output grid speci?ed by a bounding box and a cell resolution,as well as a threshold parameter k max ,the algorithm uses O (N B h M +sort(T ))I/Os,where h is the height of a quad tree on S with at most k max points in each leaf,and T is the number of cells in the desired grid DEM.Note that this is O (sort(N )+sort(T ))I/Os if h =O (log N ),that is,if the points in S are distributed such that the quad tree is roughly balanced.

The three phases of our algorithm are described in Section 2,Section 3and Sec-tion 4.In Section 2we describe how to construct a quad tree on S with at most k max points in each leaf using O (N h log M B )I/Os.The algorithm is based on the frame-work of Agarwal et.al [1].Although not as ef?cient as the algorithm by Hjaltason and Samet [9]in the worst case,we believe that it is simpler and potentially more practical;for example,it does not require computation of Morton block indices or sorting of the input points.Also in most practical cases where S is relatively nicely distributed,for example when working with LIDAR data,the two algorithms both

4P.K.Agarwal,L.Arge,and A.Danner use O (sort(N ))I/Os.In Section 3we describe how to ?nd the points in all neighbor leaves of each quad-tree leaf using O (N B h log M B )I/Os.The algorithm is simple and very similar to our quad-tree construction algorithm;it takes advantage of how the quad tree is naturally stored on disk during the segmentation phase.Note that while Hjaltason and Samet [9]do not describe a neighbor ?nding algorithm based on their Morton block approach,it seems possible to use their approach and an I/O-ef?cient priority queue [5]to obtain an O (sort(N ))I/O algorithm for the problem.However,this algorithm would be quite complex and therefore probably not of practical inter-est.Finally,in Section 4we describe how to apply an interpolation scheme to the points collected for each quad-tree leaf,evaluate the computed function at the rele-vant grid cells within the segment corresponding to each leaf,and construct the ?nal grid using O (scan(N ))+O (sort(T ))I/Os.As mentioned earlier,we can use any given interpolation method within each segment.

To investigate the practical ef?ciency of our algorithm we implemented it and experimentally compared it to other interpolation algorithms using LIDAR data.To summarize the results of our experiments,we show that,unlike existing algorithms,our algorithm scales to data sets much larger than the main memory.For example,using a 1GB machine we were able to construct a grid DEM containing 1.3billion points (occupying 1.2GB)from a LIDAR data set of over 390million points (occu-pying 20GB)in just 53hours.This data set is an order of magnitude larger than what could be handled by two popular GIS products–ArcGIS and GRASS.In addition to supporting large input point sets,we were also able to construct very large high res-olution grids;in one experiment we constructed a one meter resolution grid DEM containing more than 53billion cells—storing just a single bit for each grid cell in this DEM requires 6GB.

In Section 5we describe the details of the implementation of our theoreti-cally I/O-ef?cient algorithm that uses a regularized spline with tension interpolation method [13].We also describe the details of an existing algorithm implemented in GRASS using the same interpolation method;this algorithm is similar to ours but it is not I/O-ef?cient.In Section 6we describe the results of the experimental compar-ison of our algorithm to other existing implementations.As part of this comparison,we present a detailed comparison of the quality of the grid DEMs produced by our algorithm and the similar algorithm in GRASS that show the results are in good agreement.

2Segmentation Phase:Quad-Tree Construction Given a set S of N points contained in a bounding box [x 1,x 2]×[y 1,y 2]in the plane,and a threshold k max ,we wish to construct a quad tree T [8]on S such that each quad-tree leaf contains at most k max points.Note that the leaves of T partition the bounding box [x 1,x 2]×[y 1,y 2]into a set of disjoint areas,which we call segments .Incremental construction.T can be constructed incrementally simply by in-serting the points of S one at a time into an initially empty tree.For each point p ,

From Point Cloud to Grid DEM:A Scalable Approach 5we traverse a root-leaf path in T to ?nd the leaf v containing p .If v contains less than k max points,we simply insert p in v .Otherwise,we split v into four new leaves,each representing a quadrant of v ,and re-distribute p and the points in v to the new leaves.If h is the height of T ,this algorithm uses O (Nh )time.If the input points in S are relatively evenly distributed we have h =O (log N ),and the algorithm uses O (N log N )time.

If S is so large that T must reside on disk,traversing a path of length h may require as many as h I/Os,leading to an I/O cost of O (Nh )in the I/O-model.By storing (or blocking )the nodes of T on disk intelligently,we may be able to access a subtree of depth log B (size B )in a single I/O and thus reduce the cost to O (N h log B )I/Os.Caching the top-most levels of the tree in internal memory may also reduce the number of I/Os needed.However,since not all the levels ?t in internal memory,it is hard to avoid spending an I/O to access a leaf during each insertion,or ?(N )I/Os in total.Since sort(N ) N in almost all cases,the incremental approach is very inef?cient when the input points do not ?t in internal memory.

Level-by-level construction.A simple I/O-ef?cient alternative to the incre-mental construction algorithm is to construct T level-by-level:We ?rst construct the ?rst level of T ,the root v ,by scanning through S and,if N >k max ,distributing each point p to one of four leaf lists on disk corresponding to the child of v containing p .Once we have scanned S and constructed one level,we construct the next level by loading each leaf list in turn and constructing leaf lists for the next level of T .While processing one list we keep a buffer of size B in memory for each of the four new leaf lists (children of the constructed node)and write buffers to the leaf lists on disk as they run full.Since we in total scan S on each level of T ,the algorithm uses O (Nh )time,the same as the incremental algorithm,but only O (Nh/B )I/Os.However,

even in the case of h =log 4N ,this approach is still a factor of log M B N /log 4N from the optimal O (N B log M B N B )I/O bound.Hybrid https://www.doczj.com/doc/7710606.html,ing the framework of Agarwal et.al [1],we design a hybrid algorithm that combines the incremental and level-by-level approaches.In-

Fig.1.Construction of a quad-tree layer of depth three with k max =2.Once a leaf at depth three is created,no further splitting is done;instead additional points in the leaf are stored in leaf lists shown below shaded nodes.After processing all points the shaded leaves with more than two points are processed recursively.

6P.K.Agarwal,L.Arge,and A.Danner

stead of constructing a single level at a time,we can construct a layer of log4M

B levels.Because4log4M B=M/B

the root of the layer to a leaf of the layer is of height log4M

B .In this case,we write

all points contained in such a leaf v to a list L v on disk.After all points have been processed and the layer constructed,we write the layer to disk sequentially and re-cursively construct layers for each leaf list L i.Refer to Figure1.

Since a layer has at most M/B nodes,we can keep a internal memory buffer of size B for each leaf list and only write points to disk when a buffer runs full(for leaves that contain less than B points in total,we write the points in all such leaves to a single list after constructing the layer).In this way we can construct a layer on N

points in O(N/B)=scan(N)I/Os.Since a tree of height h has h/log4M

B layers,

the total construction cost is O(N h

log M

B )I/Os.This is sort(N)=O(N log M

B

N)

I/Os when h=O(log N).

3Neighbor Finding Phase

Let T be a quad tree on S.We say that two leaves are neighbors if their associated segments share part of an edge or a corner.Refer to Figure2for an example.If L is the set of segments associated with the leaves of T,we want to?nd for each q∈L the set S q of points contained in q and the neighbor leaves of q.As for the construction algorithm,we?rst describe an incremental algorithm and then improve its ef?ciency using a layered approach.

Fig.2.The segment q associated with a leaf of a quad tree and its six shaded neighboring segments.

Incremental approach.For each segment q∈L,we can?nd the points in the neighbors of q using a simple recursive procedure:Starting at the root v of T,we compare q to the segments associated with the four children of v.If the bounding box of a child u shares a point or part of an edge with q,then q is a neighbor of at

From Point Cloud to Grid DEM:A Scalable Approach 7least one leaf in the tree rooted in u ;we therefore recursively visit each child with an associated segment that either neighbors or contains q .When we reach a leaf we insert all points in the leaf in S q .

To analyze the algorithm,we ?rst bound the total number of neighbor segments found over all segments q ∈L .Consider the number of neighbor segments that are at least the same size as a given segment q ;at most one segment can share each of q ’s four edges,and at most four more segments can share the four corner points of q .Thus,there are at most eight such neighbor segments.Because the neighbor relation is symmetric,the total number of neighbor segments over all segments is at most twice the total number of neighbor segments which are at least the same size.Thus the total number of neighbor segments over all segments is at most 16times the num-ber of leaves of T .Because the total number of leaves is at most 4N/k max ,and since the above algorithm traverses a path of height h for each neighbor,it visits O (Nh )nodes in total.Furthermore,as each leaf contains at most k max points,the algorithm reports O (Nk max )points in total.Thus the total running time of the algorithm is O ((h +k max )N )=O (hN )).This is also the worst case I/O cost.

Layered approach.To ?nd the points in the neighboring segments of each seg-ment in L using a layered approach similar to the one used to construct T ,we ?rst load the top log 4M B levels of T into memory.We then associate with each leaf u in the layer,a buffer B u of size B in internal memory and a list L u in external mem-ory.For each segment q ∈L ,we use the incremental algorithm described above to ?nd the leaves of the layer with an associated segment that completely contains q or share part of a boundary with q .Suppose u is such a layer leaf.If u is also a leaf of the entire tree T ,we add the pair (q,S u )to a global list Λ,where S u is the set of points stored at u .Otherwise,we add q to the buffer B u associated with u ,which is written to L u on disk when B u runs full.After processing all segments in L ,we recursively process the layers rooted at each leaf node u and its corresponding list L u .Finally,after processing all layers,we sort the global list of neighbor points Λby the ?rst element q in the pairs (q,S u )stored in Λ.After this,the set S q of points in the neighboring segments of q are in consecutive pairs of Λ,so we can construct all S q sets in a simple scan of Λ.

Since we access nodes in T during the above algorithm in the same order they were produced in the construction of T ,we can process each layer of log 4M B levels of T in scan(N )I/Os.Furthermore,since q |S q |=O (N ),the total number of I/Os

used to sort and scan Λis O (sort(N )).Thus the algorithm uses O (N B h M B )I/Os in

total,which is O (sort(N ))when h =O (log N ).4Interpolation Phase

Given the set S q of points in each segment q (quad tree leaf area)and the neighboring segments of q ,we can perform the interpolation phase for each segment q in turn sim-ply by using any interpolation method we like on the points in S q ,and evaluating the

8P.K.Agarwal,L.Arge,and A.Danner computed function to interpolate each of the grid cells in q.Since q|S q|=O(N), and assuming that each S q?ts in memory(otherwise we maintain a internal memory priority queue to keep the n max

5Implementation

We implemented our methods in C++using TPIE[7,6],a library that eases the implementation of I/O-ef?cient algorithms and data structures by providing a set of primitives for processing large data sets.Our algorithm takes as input a set S of points,a grid size,and a parameter k max that speci?es the maximum number of points per quad tree segment,and computes the interpolated surface for the grid using our segmentation algorithm and a regularized spline with tension interpolation method[13].We chose this interpolation method because it is used in the open source GIS GRASS module s.surf.rst[14]—the only GRASS surface interpolation method that uses segmentation to handle larger input sizes—and provides a means to compare our I/O-ef?cient approach to an existing segmentation method.Below we discuss two implementation details of our approach:thinning the input point set,and supporting a bit mask.Additionally,we highlight the main differences between our implementation and s.surf.rst.

Thinning point sets.Because LIDAR point sets can be very dense,there are often several cells in the output grid that contain multiple input points,especially when the grid cell size is large.Since it is not necessary to interpolate at sub-pixel resolutions,computational ef?ciency improves if one only includes points that are suf?ciently far from other points in a quad-tree segment.Our implementation only includes points in a segment that are at least a user-speci?ed distanceεfrom all other points within the segment.By default,εis half the size of a grid cell.We implement this feature with no additional I/O cost simply by checking the distance between a new point p and all other points within the quad-tree leaf containing p and discarding p if it is within a distanceεof another point.

Bit mask.A common GIS feature is the ability to specify a bit mask that skips computation on certain grid cells.The bit mask is a grid of the same size as the output grid,where each cell has a zero or one bit value.We only interpolate grid cell

From Point Cloud to Grid DEM:A Scalable Approach 9values when the bit mask for the cell has the value one.Bit masks are particularly useful when the input data set consists of an irregularly shaped region where the input points are clustered and large areas of the grid are far from the input points.Skipping the interpolation of the surface in these places reduces computation time,especially when many of the bit mask values are zero.

For high resolution grids,the number of grid cells can be very large,and the bit mask may be larger than internal memory and must reside on disk.Randomly querying the bit mask for each output grid cell would be very expensive in terms of I/O https://www.doczj.com/doc/7710606.html,ing the same ?ltering idea described in Section 2and Section 3,we ?lter the bit mask bits through the quad-tree layer by layer such that each quad-tree segment gets a copy of the bit mask bits it needs during interpolation.The algorithm uses O (T B h log M

B

)I/Os in total,where T is the number of cells in the output grid,which is O (sort(T ))when h =O (log N ).The bits for a given segment can be accessed sequentially as we interpolate each quad-tree segment.

GRASS Implementation.The GRASS module s.surf.rst uses a quad-tree segmentation,but is not I/O-ef?cient in several key areas which we brie?y discuss;constructing the quad tree,supporting a bit mask,?nding neighbors,and evaluating grid cells.All data structures in the GRASS implementation with the exception of the output grid are stored in memory and must use considerably slower swap space on disk if internal memory is exhausted.During construction points are simply inserted into an internal memory quad tree using the incremental construction approach of Section 2.Thinning of points using the parameter εduring construction is imple-mented exactly as our implementation.The bit mask in s.surf.rst is stored as a regular grid entirely in memory and is accessed randomly during interpolation of segments instead of sequentially in our approach.

Points from neighboring quad-tree segment are not found in advance as in our algorithm,but are found when interpolating a given quad-tree segment q ;the algo-rithm creates a window w by expanding q in all directions by a width δand querying the quad tree to ?nd all points within w .The width δis adjusted by binary search until the number of points within w is between a user speci?ed range [n min ,n max ].Once an appropriate number of points is found for a quad-tree segment q ,the grid cells in q are interpolated and written directly to the proper location in the output grid by randomly seeking to the appropriate ?le offset and writing the interpolated results.When each segment has a small number of cells,writing the values of the T output grid cells uses O (T ) sort(T )I/Os.Our approach constructs the output grid using the signi?cantly better sort(T )I/Os.6Experiments

We ran a set of experiments using our I/O-ef?cient implementation of our algorithm and compared our results to existing GIS tools.We begin by describing the data sets on which we ran the experiments,then compare the ef?ciency and accuracy of our algorithm with other methods.We show that our algorithm is scalable to over 395

10P.K.Agarwal,L.Arge,and A.Danner

(a)(b)

Fig.3.(a)Neuse river basin data set and(b)Outer Banks data set,with zoom to very small region.

million points and over53billion output grid cells–well beyond the limits of other GIS tools we tested.

Experimental setup.We ran our experiments on an Intel3.4GHz Pentium4 hyper-threaded machine with1GB of internal memory,over4GB of swap space,and running a Linux2.6kernel.The machine had a pair of400GB SATA disk drives in a non-RAID con?guration.One disk stored the input and output data sets and the other disk was used for temporary scratch space.

For our experiments we used two large LIDAR data sets,freely available from online sources;one of the Neuse river basin from the North Carolina Floodmaps project[15]and one of the North Carolina Outer Banks from NOAA’s Coastal Ser-vices Center[16].

Neuse river basin.This data set contains500million points,more than20GB of raw data;see Figure3(a).The data have been pre-processed by the data providers to remove most points on buildings and vegetation.The average spacing between points is roughly20ft.

Outer banks.This data set contains212million LIDAR points,9GB of raw data; see Figure3(b).Data points are con?ned to a narrow strip(a zoom of a very small portion of the data set is shown in the?gure).This data set has not been heavily pre-processed to remove buildings and vegetation.The average point spacing is roughly 3ft.

Scalability results.We ran our algorithm on both the Neuse river and Outer Banks data sets at varying grid cell resolutions.Because we used the default value ofε(half the grid cell size)increasing the size of grid cells decreased the number of points in the quad tree and the number of points used for interpolation.Results

From Point Cloud to Grid DEM:A Scalable Approach11 are summarized in Table1.In each test,the interpolation phase was the most time-consuming phase;interpolation consumed over80%of the total running time on the Neuse river basin data set.For each test we used a bit mask to ignore cells more than300ft from the input points.Because of the irregular shape of the Outer Banks data,this bit mask is very large,but relatively sparse(containing very few“1”bits). Therefore,?ltering the bit mask and writing the output grid for the Outer Banks data were relatively time-consuming phases when compared to the Neuse river data. Note that the number of grid cells in the Outer Banks is roughly three orders of magnitude greater than the number of quad-tree points.As the grid cell size decreases and the total number of cells increases,bit mask and grid output operations consume a greater percentage of the total time.At a resolution of5ft,the bit mask alone for the Outer Banks data set is over6GB.Even at such large grid sizes,interpolation—an internal memory procedure—was the most time-consuming phase,indicating that I/O was not a bottleneck in our algorithm.

Dataset Neuse Outer Banks

Resolution(ft)2040510

Output grid cells(×106)13603405316013402

quad-tree points(×106)39523612866

Total Time(hrs)53.024.417.7 6.9

Time spent to...(%)

Build tree 2.0 3.8 4.58.6

Find Neighbors10.615.114.516.4

Filter Bit mask0.20.313.18.0

Interpolate86.480.452.657.8

Write Output0.80.415.39.2

Table1.Results from the Neuse river basin and the Outer Banks data sets.

We also tried to test other available interpolation methods,including s.surf.rst in the open source GIS GRASS;kriging,IDW,spline,and topo-to-raster(based on ANUDEM[10])tools in ArcGIS9.1;and QTModeler4from Applied Imagery[3]. Only s.surf.rst supported the thinning of data points based on cell size,so for the other programs we simply used a subset of the data points.None of the ArcGIS tools could process more than25million points from the Neuse river basin at20ft resolution and every tool crashed on large input sizes.The topo-to-raster tool pro-cessed the largest set amongst the ArcGIS tools at21million points.

The s.surf.rst could not process more than25million points https://www.doczj.com/doc/7710606.html,ing a resolution of200ft,s.surf.rst could process the entire Neuse data set in six hours,but the quad tree only contained17.4million points.Our algorithm processed the same data set at200ft resolution in3.2hours.On a small subset of the Outer Banks data set containing48.8million points,s.surf.rst,built a quad tree on 7.1million points and computed the output grid DEM in three hours,compared to 49minutes for our algorithm on the same data set.

12P.K.Agarwal,L.Arge,and A.Danner The QTModeler program processed the largest data set amongst the other meth-ods we tested,approximately50million points,using1GB of RAM.The documen-tation for QTModeler states that their approach is based on an internal memory quad tree and can process200million points with4GB of available RAM.We can process a data set almost twice as large using less than1GB of RAM.

Overall,we have seen that our algorithm is scalable to very large point sets and very large grid sizes and we demonstrated that many of the commonly used GIS tools cannot process such large data sets.Our approach for building the quad tree and?nding points in neighboring segments is ef?cient and never took more than 25%of the total time in any of our experiments.The interpolation phase,an internal step that reads points sequentially from disk and writes grid cells sequentially to disk, was the most time-consuming phase of the entire algorithm.

Comparison of constructed grids.To show that our method constructs cor-rect output grids,we compared our output on the Neuse river basin to the original input points as well as to grid DEMs created by s.surf.rst,and DEMs freely available from NC Floodmaps.Because s.surf.rst cannot process very large data sets,we ran our tests on a small subset of the Neuse river data set containing 13million points.The output resolution was20ft,εwas set to the default10ft,and the output grid had3274rows and3537columns for a total of11.6million cells. Approximately11million points were in the quad tree.

Fig.4.Distribution of deviations between input points and interpolated surface(k max=35).

The interpolation function we tested used a smoothing parameter and allowed the input points to deviate slightly from the interpolated surface.We used the same default smoothing parameter used in the GRASS implementation and compared the distribution of deviations between the input points and the interpolated surface.The results were independent of k max,the maximum number of points per quad-tree segment.In all tests,at least79%of the points had no deviation,and over98%of the points had a deviation of less than one inch.Results for s.surf.rst were similar.

From Point Cloud to Grid DEM:A Scalable Approach13 Since the results were indistinguishable for various k max parameters,we show only one of the cumulative distribution functions(CDF)for k max=35in Figure4.

Next,we computed the absolute deviation between grid values computed using s.surf.rst and our method.We found that over98%of the cells agreed within1 inch,independent of k max.The methods differ slightly because s.surf.rst uses a variable size window to?nd points in neighboring points of a quad-tree segment q and may not choose all points from immediate neighbors of q when the points are dense and may expand the window to include points in segments that are not immediate neighbors of q when the points are sparse.In Figure5(a)we show a plot of the interpolated surface along with an overlay of cells where the deviation exceeds 3inches.Notice that most of the bad spots are along the border of the data set where our method is less likely to get many points from neighboring quad-tree leaves and near the lake in the upper left corner of the image where LIDAR signals are absorbed by the water and there are no input data points.

(a)(b)

Fig.5.Interpolated surface generated by our method.Black dots indicate cells where the devi-ation between our method and(a)s.surf.rst is greater than three inches,(b)nc?oodmap data is greater than two feet.

Finally,we compared both our output and that of s.surf.rst to the20ft DEM data available from the NC Floodmaps project.A CDF in Figure6of the absolute deviation between the interpolated grids and the“base”grid from NC Floodmaps shows that both implementations have an identical CDF curve.However,the agree-ment between the interpolated surfaces and the base grid is not as strong as the agree-ment between the algorithms when compared to each other.An overlay of regions with deviation greater than two feet on base map shown in Figure5(b)reveals the source of the disagreement.A river network is clearly visible in the?gure indicat-ing that something is very different between the two data sets along the rivers.NC Floodmaps uses supplemental break-line data that is not part of the LIDAR point set

14P.K.Agarwal,L.Arge,and A.Danner to enforce drainage and provide better boundaries of lakes in areas where LIDAR has trouble collecting data.Aside from the rivers,the interpolated surface generated by either our method or the existing GRASS implementation agree reasonably well with the professionally produced and publicly available base map.

Fig.6.Cumulative distribution of deviation between interpolated surface and data downloaded from nc?https://www.doczj.com/doc/7710606.html,.Deviation is similar for both our method and s.surf.rst for all values of k max.

7Conclusions

In this paper we describe an I/O-ef?cient algorithm for constructing a grid DEM from point cloud data.We implemented our algorithm and,using LIDAR data,ex-perimentally compared it to other existing algorithms.The empirical results show that,unlike existing algorithms,our approach scales to data sets much larger than the size of main memory.Although we focused on elevation data,our technique is general and can be used to compute the grid representation of any bivariate function from irregularly sampled data points.

For future work,we would like to consider a number of related problems.Firstly, our solution is constructed in such a way that the interpolation phase can be executed in parallel.A parallel implementation should expedite the interpolation procedure. Secondly,as seen in Figure5(b),grid DEMs are often constructed from multiple sources,including LIDAR points and supplemental break-lines where feature preser-vation is important.Future work will examine methods of incorporating multiple data sources into DEM construction.Finally,the ability to create large scale DEMs ef?-ciently from LIDAR data could lead to further improvements in topographic analysis including such problems as modelling surface water?ow or detecting topographic change in time series data.

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