# PickShift: A user-friendly Python tool to assess the surficial uncertainties associated with polygons extracted from historical planimetric data

Timothée Jautzy, Pierrick Freys, Valentin Chardon, Romain Wenger, Gilles Rixhon, Laurent Schmitt, Pierre-Alexis Herrault

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## To cite this version:

Timothée Jautzy, Pierrick Freys, Valentin Chardon, Romain Wenger, Gilles Rixhon, et al. PickShift: A user-friendly Python tool to assess the surficial uncertainties associated with polygons extracted from historical planimetric data. SoftwareX, 2024, 27, pp.101866. 10.1016/j.softx.2024.101866. hal-05295824

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Original Software Publication

# PickShift: A user-friendly Python tool to assess the surficial uncertainties associated with polygons extracted from historical planimetric data

Timothée Jautzy<sup>a,*</sup>, Pierrick Frees<sup>a,b</sup>, Valentin Chardon<sup>a</sup>, Romain Wenger<sup>a</sup>, Gilles Rixhon<sup>a</sup>, Laurent Schmitt<sup>a</sup>, Pierre-Alexis Herrault<sup>a</sup>

<figure><img src="image_3.png" /></figure>

## ARTICLE INFO

### Keywords:

Historical maps

Orthophotos

Surficial uncertainty

Geometric error

Monte-Carlo

## ABSTRACT

With the increasing use of GIS software's, historical planimetric data such as orthophotos and old maps represent key data sources to analyze spatio-temporal landscape evolution. However, geometric error inherent to these data are too often overlooked, possibly leading to confusing misinterpretation of measured surficial changes. The user-friendly Python tool 'PickShift', based on a Monte-Carlo approach, addresses this critical issue by quantifying the surficial uncertainty associated with any features digitized from historical planimetric data. This software provides a valuable framework for a more accurate assessment of landscape dynamics and associated uncertainties.

## Metadata

<table><thead><tr><td>Nr</td><td>Code metadata description</td><td></td></tr></thead><tbody><tr><td>C1</td><td>Current code version</td><td>v1.0</td></tr><tr><td>C2</td><td>Permanent link to code/repository used for this code version</td><td>https://github.com/r-wenger/Pickshift</td></tr><tr><td>C3</td><td>Permanent link to reproducible capsule</td><td>https://seafile.unistra.fr/d/841b64f415a649c98ca8/</td></tr><tr><td>C4</td><td>Legal code license</td><td>GPL-3.0</td></tr><tr><td>C5</td><td>Code versioning system used</td><td>'none'</td></tr><tr><td>C6</td><td>Software code languages, tools and services used</td><td>python</td></tr><tr><td>C7</td><td>Compilation requirements, operating environments and dependencies</td><td>/</td></tr><tr><td>C8</td><td>If available, link to developer documentation/manual</td><td>https://github.com/r-wenger/Pickshift</td></tr><tr><td>C9</td><td>Support email for questions</td><td>timothee.jautzy@live.cnrs.unistra.fr</td></tr></tbody></table>

## 1. Motivation and significance

With the increasing use of geographic information system (GIS) software's, historical planimetric data (HPD), such as aerial photographs, orthophotos and historical maps, have become a major source of data to explore the spatio-temporal evolution of landscapes [1,2]. This

approach primarily consists in manually digitizing (e.g. [3,4]) or automatically extracting (e.g. [5-7]) features of interest from diachronic, coregistrated planimetric data to analyze the decadal to centennial evolution of these features. It allows addressing various socio-environmental topics focusing on a wide range of extractable features, such as forests fragments [5], ecotones [3], river channels [8], valley glaciers [9], urban objects [10] and, more generally, land cover [11,12]. It can eventually improve environmental management practices in contexts where quantifying impacts of human activities and climate change on socio-ecological systems helps to better understand landscape dynamics [13]. Moreover, this approach is simple and efficient since it only requires a GIS software and diachronic HPD, both becoming widely and freely available. For instance, the French National Geographic Institute (IGN) recently made available two series of aerial photographs (1950, 2018) and a historical map ('Etat-Major'; 19th century) covering the whole French territory in a coregistrated format [14].

However, the process of data coregistration, along with the initial cartographic production and the potential quality loss of the underlying paper map, inevitably distorts the raw data so that geometric errors are inherently associated with HPD [15,16]. Surprisingly, many studies dealing with the spatio-temporal evolution of landscapes or landforms from HPD either do not consider these geometric errors (e.g. [11,17,18]), or simply report the root mean square error (RMSE) calculated

<footnote><sup>a</sup> Laboratoire Image, Ville, Environnement (LIVE UMR 7362), Université de Strasbourg, CNRS, 3 rue de l'Argonne, Strasbourg 67083, France</footnote>

<footnote><sup>b</sup> Eurométropole de Strasbourg, Service Géomatique et Connaissance du territoire, 1 parc de l'Etoile, Strasbourg 67076, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: timothee.jautzy@live-cnrs.unistra.fr (T. Jautzy).</footnote>
from the ground control points (GCP) used to coregister the initial data. This last approach assumes a uniform geometric error over the whole study area (e.g. [9,19–21]), although a spatial variability intrinsically affects these errors associated with HPD [16,22]. In fluvial contexts where the amplitude of channel-planform changes might be too low to be detected [23], Jautzy et al. [24] and Leonard et al. [25] demonstrated that considering the spatially variable error (SVE) allows to determine the (non-)significance of measured channel planform changes, such as lateral channel migration. Both developed a reproducible method to evaluate the impact of SVE on channel-planform changes measured from diachronic, coregistrated aerial photographs [25] or orthophotos [24]. Although the useful approach of both previous studies can be theoretically transposed to other features, they only focused on fluvial settings and none of them delivered a user-friendly tool in an open-source language.

We argue that these automatized approaches should be extended to any kind of features extractable from HPD to detect (i) the uncertainty of surfaces measured in different environmental settings and (ii) the (non-) significance of planimetric changes over time. We thus propose a python-based tool, called PickShift, to improve the generalization of methods allowing for the assessment of uncertainties associated with HPD [26]. Based on a Monte-Carlo approach, it is an enhanced version of Jautzy et al.’s [24] approach and assesses the uncertainties associated with the surfaces of features (i.e. polygons) digitized from any targeted HPD (i.e. aerial photographs, orthophotos and historical maps), compared to reference planimetric data (i.e. recent orthophoto). Its denomination merges the twofold action of ‘pick’ing GCPs and randomly ‘shift’ing the polygons according to the SVE affecting the targeted data.

## 2. Software description

PickShift’s main achievement allows computing the surficial uncertainty of polygons digitized from HPD. The script is based on Jautzy et al.’s [24] method, that we implemented in a user-friendly and open-source tool. Its simple design thus does not require specific Python skills to be used. The complete installation and operation guidelines, as well as a video tutorial, are provided in the Github. PickShift consists of three files:

- environment.xml

It allows the operator to automatically configure and build an Anaconda environment that suits the appropriate operating system.

- config.conf

It contains the location of the required inputs and a few parameters that can be modified by the operator.

- pickshift.py

It is the main source code that has to be run once the Anaconda environment is created and the configuration file parameterized.

PickShift also requires three input files that must be prepared by the operator using any GIS software (e.g. QGIS, ArcGIS):

- a shapefile of the spatial extent of the studied area

- a shapefile of the features digitized from the HPD

- a set of independent ground control points

### 2.1. Software architecture

PickShift consists of two main steps and one optional step. It first interpolates the SVE over the study area and then assesses the surficial uncertainty of each polygon (Fig. 1). The optional step allows simplifying the polygons’ geometry, if needed.

#### 2.1.1. Spatially-variable error (SVE) computing (step 1)

The software first measures the planimetric bias (i.e. Euclidean distance) for each pair of GCPs, by comparing the geographic coordinates picked from the reference planimetric data with those picked from the targeted data. Local planimetric biases are then interpolated over the spatial extent of the whole study area, using an inverse distance weighting method (Fig. 1). The software then exports three rasters corresponding to the SVE in X, Y and XY. The raster cell size and the coordinates system of the SVE rasters are set by the operator. The mean SVE is then compared with the mean distance between the successive vertices of all polygons. If this ratio is >1, the operator is warned that subsequent Monte-Carlo (MC) simulations may cause topological errors. The operator must choose between ignoring the warning to continue the analysis, or restarting it after setting Douglas-Peucker simplification parameters (optional step; see part 2.1.3).

#### 2.1.2. Monte-Carlo simulations and assessment of the surficial uncertainty of each polygon (step 2)

For each polygon vertex, the values of all pixels from the SVE rasters within a buffer are selected. The size of the buffer is set by the operator. The normal distribution of SVE around each vertex is then calculated by averaging the mean local error and by calculating the standard deviation. Hence, for each MC run, a specific value of SVE in X and Y is randomly extracted from the respective normal distribution and used to shift each vertex from its original position (Fig. 1). The direction of the translation in X and Y also are randomly selected at each MC run with equal probability weights (i.e. 50% each). The coordinates of the translated vertices are stored for each run. The number of runs is set by the operator.

The simulation process can be represented by the following mathe-

<figure><img src="image_4.png" /><figcaption>Fig. 1. PickShift workflow.</figcaption></figure>
matical expressions:

$$X_{\text{changed}} = X_{\text{original}} + |e_x| \times \beta_1 + e_d \times \beta_2$$

$$Y_{\text{changed}} = Y_{\text{original}} + |e_y| \times \beta_1 + e_d \times \beta_2$$

Here, $|e_x|$ and $|e_y|$ represent the absolute registration errors in the X and Y directions, respectively. The constant $e_d$ represents the digitizing error, which is set by the operator. $\beta_1$ and $\beta_2$ are coefficients that determine the direction of the registration error (i.e., $|e_x|$ and $|e_y|$) and the digitizing error ($e_d$), and they are randomly selected during each run. Note that for each polygon, $\beta_1$ remains constant for each $X_{\text{changed}}$ (i.e. constrained in only one direction). This constraint allows to maintain the directional sequence of successive vertices [24].

Once the Monte-Carlo simulations are completed, polygons are reconstructed (poly_sim MC.csv) from the stored coordinates (point-sim MC.csv). If the operator chooses to simplify the reconstructed polygons, the Douglas-Peucker algorithm is applied according to the tolerance parameter (see part 2.1.3). The area of the reconstructed polygons is then computed, along with a series of statistics associated with each of the initial polygons. PickShift automatically computes the following statistics from the total range of measured values compiled from all MC runs:

- average area;

- standard deviation;

- minimum and maximum;

- first, second and third quartile;

- total and 95% uncertainty [24].

Finally, the software exports the final shapefile (poly MC.gpkg), corresponding to the initial shapefile to which the previously calculated statistics are appended.

### 2.1.3. Douglas-Peucker simplification (optional step)

This optional step is required if the ratio between the mean SVE and the mean distance between vertices is >1 (Fig. 1). In that case, Monte-Carlo simulations may cause topological errors, leading to fundamental incorrectness in the calculation of polygon surfaces. A practical solution consists in performing geometric simplification of the reconstructed polygons, after the Monte-Carlo simulations. PickShift implements the Douglas-Peucker algorithm [27], subdividing the initial line or polygon into smaller segments and joining their endpoints with straight lines. Then, it removes any points closer to the straight line than a specified tolerance threshold. If the aforementioned ratio is >1, PickShift automatically suggests a tolerance value to be set by the operator. The tolerance suggestion corresponds to twice the mean distance between vertices.

### 2.2. Technical recommendations

PickShift is designed to quantify the SVE of coregistrated data, from a set of GCPs selected by the operator. If the latter coregistrates the data, the set of GCPs used to interpolate the SVE must be independent from the one initially used to coregistrate the data [24,28]. GCPs should be uniformly distributed over the whole study area and hard features (e.g. buildings, roads...) are preferred over soft ones (e.g. pathway intersections, trees...). A minimum of six to eight GCPs is recommended as, beyond this threshold, the quality of error characterization is not improved [28].

We recommend interpolating the SVE with a raster cell size equal to that of the targeted planimetric data. A digitizing error of at least one pixel (referring to the targeted data) is considered reasonable since the user is not able to see details below this value. Concerning the buffer radius used to extract SVE values around vertices, we suggest applying ten times the raster cell size of the targeted planimetric data. Finally, if a

Douglas-Peucker simplification is performed, we strongly recommend a visual inspection of the simplification results before any interpretation of the surficial uncertainty results. We suggest to apply first a tolerance value equal to twice the average distance between vertices and to gradually increase it as long as topological errors persist. One must keep in mind that any geometrical simplification necessarily results in a degradation of the initial data (see part 3).

## 3. Illustrative examples

Both examples presented here aim to illustrate the use of PickShift on two types of data widely used in planimetric studies: a coregistrated historical map and an orthophoto. Contrasting characteristics in terms of raster cell size, geometric error, and topography are presented to highlight the specificities of their use in PickShift. We also aim to assess the impact of polygons simplification using the Douglas-Peucker algorithm (part 2.1.3). The targeted planimetric data from the first dataset (Munster; Table 1; Fig. 2A) is the ‘Etat Major’ map, produced between 1820 and 1856 and covering the whole French territory. It is provided by the French IGN in a coregistrated format (EPSG:2154) and has a raster cell size of 4 m. The targeted planimetric data from the second dataset (Bruche; Table 1; Fig. 2B) is an aerial photograph surveyed in 1950 and covering the whole French territory. It is provided by the French IGN in an orthorectified format (EPSG:3948) and has a raster cell size of 0.50 m. For both datasets, the reference planimetric data used to create the GCP set is an orthophoto from 2015 provided by the French IGN with a raster cell size of 0.20 cm.

PickShift was applied on both datasets (Munster and Bruche) to assess the surficial uncertainty of digitized polygons through 1000 Monte-Carlo simulations, with and without an additional Douglas-Peucker simplification. The same three types of polygons were digitized from both datasets: river channel, cropland and forest (Fig. 2). The buffer was set to ten times the raster cell size of the targeted planimetric data. The raster cell size of the SVE rasters and the digitization error were set to the raster cell size of the targeted planimetric data.

### 3.1. Munster dataset

This dataset is composed of sixteen polygons manually digitized from the ‘Etat major’ map (1820/1856) centered around the town of Munster along the Fecht valley (Haut-Rhin/France; Fig. 2A). High topographic variations characterize this area (Table 1). We selected eighteen GCPs to estimate the planimetric bias between the targeted (‘Etat major’ map) and the reference (2015 orthophoto) planimetric data (Fig. 2A). The mean planimetric bias in XY amounts to 34.38 m (± 27.80 m). The interpolated SVE ranges from 5.9 to 94.8 m (Fig. 2B). Here, the ratio between the mean SVE and the mean distance between vertices is >1 (i. e. 3.2). Fig. 3A and 3B shows the first ten translations of polygon #3 resulting from Monte-Carlo simulations without and with an additional Douglas-Peucker simplification, respectively. One must bear in mind, however, the incorrectness of calculating the area of a polygon whose geometry contains topological errors. Here, the Douglas-Peucker simplification is mandatory to remove these. We used a tolerance of 50 m. The total surficial uncertainty associated to the polygons without and with an additional Douglas-Peucker simplification ranges from 4.06 to 39.98% and 7.44 to 62.80%, respectively (Fig. 4A).

### 3.2. Bruche dataset

This dataset is composed of twelve polygons manually digitized from an orthophoto (1950) centered on the Bruche River’s floodplain located five kilometers westward of Strasbourg city (Bas-Rhin / France; Fig. 2C). Low topographic variations characterize this area (Table 1). We selected twelve GCPs to estimate the planimetric bias between the targeted (1950 orthophoto) and the reference (2015 orthophoto) planimetric data (Fig. 2C). The mean planimetric bias in XY amounts to 0.92 m (± 0.45
<table><caption>**Table 1**
Main characteristics of the two datasets.</caption><thead><tr><th>Dataset</th><th>Targeted planimetric data</th><th>Raster cell size (m)</th><th>Projection system (EPSG)</th><th>Topographic variation (m)</th><th>Number of GCP</th><th>Number of polygons</th><th>Mean planimetric bias XY (m)</th></tr></thead><tbody><tr><td>Munster</td><td>État major map (1820/1856)</td><td>4</td><td>Lambert 93 (2154)</td><td>mean:480.4<br>std: 84.5<br>min: 339.7<br>max: 785.5</td><td>18</td><td>16</td><td>34.4 ± 27.8</td></tr><tr><td>Bruche</td><td>Orthophoto (1950)</td><td>0.50</td><td>RGF93/CC48 (3948)</td><td>mean:143.8<br>std: 1.3<br>min: 139.1<br>max: 148.4</td><td>12</td><td>12</td><td>0.9 ± 0.5</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Datasets used in the illustrative examples showing the three types of polygons digitized from the targeted planimetric data, GCP set and interpolated SVE for the Munster (A-B) and the Bruche dataset (C-D).</figcaption></figure>

m). The interpolated SVE ranges from 0.27 to 1.90 m (Fig. 2D). As the ratio between the mean SVE and the mean distance between vertices is <1 (i.e. 0.05), the optional step (Douglas-Peucker) is not necessary but it was performed for the sake of comparison with the first dataset (Fig. 3C-D). We used a tolerance of 1 m. The total surficial uncertainty associated with the polygons ranges from 1.28 to 4.80% and 1.18 to 5.04% without and with an additional Douglas-Peucker simplification, respectively (Fig. 4B).

## 4. Impact

PickShift is a valuable tool enhancing the reliability of studies based on HPD to reconstruct spatio-temporal landscape dynamics. It allows to quantify the inherited part of SVE in the landscape variables derived from each planimetric data individually. It also offers the opportunity of investigating its impact in the calculation of dynamic landscape

variables derived from several sources which further limit error propagation in computing models. Beyond the scope of spatial modeling, the originality of PickShift lies in its ability to consider each source of uncertainty together: the production-; transformation-, and application-oriented uncertainty [29,30]. The two first sources are simultaneously considered in the bias calculated from GCPs. Quantifying the respective contribution of coregistration and inherent quality source in the positional error estimates is a challenging task. The third source is considered through the calculation of indicators of relative uncertainty. They can be used in the decision process by users to measure the conformity of derived estimates regarding initial purposes. Furthermore, we suggest that future developments of PickShift should focus on the incorporation of different, SVE components, such as digitization. Given that the extraction of surface data from HPDs is becoming increasingly automated [7], we believe that the next step would be to integrate our program into automatic extraction algorithms. Connections with
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Examples of ten simulations for polygon #3 of the Munster dataset, without (A) and with (B) Douglas-Peucker simplification and for polygon #2 of the Bruche dataset, without (C) and with (D) Douglas-Peucker simplification.</figcaption></figure>

existing tools to extract information from HPD should thus be consid-
ered (e.g. Plugin QGIS Historical Map, https://plugins.qgis.org/plugins/HistoricalMap/ [5]; Package R HistMapR, https://github.com/AGAuffret/HistMapR [31]; Package Python HMVT, https://github.com/EVS-GIS/hmvt-cli [32]). These PickShift enhancements would pave the way for a global analysis of geometric uncertainties in surficial estimates derived from HPD.

We argue that PickShift could become a new paradigm in the pro-
cessing of HPD. First, through its simplicity and availability, we expect
more systematic measurements of spatial error and positive outcomes in
historical land-cover studies. Second, we expect higher precision and
transparency in the derived estimates. Third, we expect an increasing
use of HPD since that can be accompanied by information about the
quality and the reliability of output databases. Further bibliographical
reviews and download analysis should be achieved to validate these
expectations.

5. Conclusions

The use of historical planimetric data is essential for reconstructing

landscape dynamics and anticipating future environmental changes.
With the increasing availability of historical planimetric data and open-
source GIS software's, academic expertise is no longer required to use
these data. However, the spatially variable error that inherently affects
these data must be carefully considered. If not, the measured surface
changes may be misinterpreted. The user-friendly Python tool PickShift
addresses this issue: it allows (1) computing and spatializing the geo-
metric error associated with historical planimetric data, and (2) quan-
tifying the surficial uncertainty associated with any digitized features.
PickShift is also a versatile tool. On the one hand, its simple design al-
lows using it without advanced GIS skills (e.g. environmental managers
or students). On the other hand, its open code allows technical en-
hancements for more sophisticated studies encompassing the different
facets of uncertainty in spatial models, such as (i) propagating uncer-
tainty, (ii) modeling uncertainty on model inputs, (iii) resulting uncer-
tainty on model outputs, and (iv) estimating variance-based sensitivity
indices. We finally argue that PickShift can contribute to the general-
ization of methods for assessing uncertainties in the analysis of historical
planimetric data and improve our understanding of spatio-temporal
landscape dynamics, thereby contributing to more efficient
<figure><img src="image_7.png" /><figcaption>Fig. 4. Surficial uncertainty associated with digitized polygons from Munster (A) and Bruche (B) dataset, without and with applying the Douglas-Peucker simplification.</figcaption></figure>

environmental management practices.

CRediT authorship contribution statement

**Timothée Jautzy:** Conceptualization, Data curation, Formal analysis, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. **Pierrick Freys:** Conceptualization, Methodology, Software. **Valentin Chardon:** Conceptualization, Data curation, Formal analysis, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing. **Romain Wenger:** Formal analysis, Methodology, Software, Writing – review & editing. **Gilles Rixhon:** Conceptualization, Funding acquisition, Validation, Writing – review & editing. **Laurent Schmitt:** Conceptualization, Funding acquisition, Writing – review & editing. **Pierre-Alexis Herrault:** Conceptualization, Formal analysis, Methodology, Software, Supervision, Validation, Writing – original draft, Writing – review & editing.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Timothee Jautzy reports financial support was provided by University of Strasbourg. Timothee Jautzy reports financial support was provided by Graduate School of Water and Environmental Engineering. Timothee

Jautzy reports financial support was provided by La Région Grand Est. Timothee Jautzy reports financial support was provided by Regional Water Agency Rhin-Meuse. Timothee Jautzy reports financial support was provided by River Union Haute Alsace. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

Data will be made available on request.

Acknowledgments

We would like to thank Mylène Moritz for preparing the inputs of the illustrative examples. This work has been produced with free and open-source programs (QGIS, Python, R, Inkscape, LibreOffice).

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