2.1.

Study Area

This paper discusses the development of an operational urban LC mapping method that is applicable to the whole Walloon region ($\mathrm{16,844}{\mathrm{km}}^2$). In collaboration with end-users, we defined two subsets that are representative of the Walloon urban dense landscape (Fig. 1).

Fig. 1

Liège study area and samples for 2012. The blue border delineates the $25\text{-}{\mathrm{km}}^2$ representative area (zone A) used for methods development. The red border delineates the $261\text{-}{\mathrm{km}}^2$ area (zone B) used for transferability assessment. The validation samples are represented by purple squares. The training samples for the OBIA-automated approach are represented by yellow dots. A true-color composite of aerial photographs from 2012 is used as background.

Zone A is a $25\text{-}{\mathrm{km}}^2$ square area selected for the development of the two methods and for identifying the data and features of interest. Zone B is used to assess the regional transferability of the chosen methods. It corresponds to a $261\text{-}{\mathrm{km}}^2$ subset that includes the city of Liège and eight other municipalities. Zone B demonstrates a strong urbanization with 55% of artificialized areas. This value is well above the Walloon region’s mean of 15%.³³ The population density of 1663 inhabitants per ${\mathrm{km}}^2$ is largely higher than the Walloon average of 213.8 inhabitants per ${\mathrm{km}}^2$.³⁴ Zone B covers a large diversity of urban morphologies ranging from isolated house neighborhoods to $10+$ floor buildings. Residential, industrial, and commercial areas cover 55% of the area. Crops and pastures account for 30%, seminatural spaces and forests for 13%, and water bodies for 2%.³³ Given the inner complexity of urban areas,¹⁷ we believe that applying our method first to such dense urban areas and then to rural ones in a later stage is more straightforward than the inverse approach.

2.2.

Data

An operational LC mapping method should rely on routinely acquired EO data to minimize the acquisition costs and to exploit existing data as recommended by the European legislation on reuse of public sector information (PSI directive; 2003/98/CE, consolidated by 2013/37/EU). In this perspective, this study valorizes existing data even though their acquisition conditions were not always the most relevant for the discrimination of LC classes. Wallonia, as many other EU regions, acquires remotely sensed data that are particularly interesting for LC mapping. In our comparisons, we integrated three available remotely sensed datasets: (1) visible–near-infrared (VNIR) AOP (4 bands) with a spatial resolution of 0.25 m after pan-sharpening of panchromatic (PAN: 0.25 m) and multispectral (XS: 0.75 cm) bands (leaf-on: May 2012) along with a 1-m resolution nDSM generated by photogrammetry;³⁵ (2) VNIR Pléiades satellite data with a spatial resolution of 2 m for the XS bands and of 0.5 m for the PAN band (leaf-on: August 2013); and (3) the digital terrain model (DTM) and the digital surface model (DSM) generated by the Walloon authorities using the low-resolution LiDAR data acquired during the winter of 2012/2013 (leaf-off: 1 to 3 points per ${\mathrm{m}}^2$).

Ancillary vector data consist of the polygonal delineations of building footprints available in the BelMap database (GIM, 2015) and of fresh water courses and water bodies, road networks (i.e., aggregated polygons of the travel lane, sidewalk, planting strip, and on-street parking) and rail networks available in the Projet Informatique de Cartographie Continue (PICC, in French) database.³⁶ Produced using a photogrammetric method, the PICC is the Walloon digital cartographic reference at 1:1000 scale. It includes landscape elements, such as buildings, networks, infrastructures, and isolated trees, with a planimetric accuracy of 12 cm and an altimetric accuracy of 25 cm. We chose the BelMap database for delineating buildings because it was updated in 2015, due to various commercial datasets, while the PICC has not been updated since the 1990s in some parts of the region. The update of the PICC is currently being carried out by the regional authorities who consider this dataset the future spatial and thematic reference, including for buildings. This will guarantee the availability of up-to-date polygonal data that will be used for mapping the LC.

2.3.

Methods

2.3.5.

OBIA-rule-based classification

Using works of O’Neil-Dunne et al.²³ and Van De Kerchove et al.⁵⁶ as references, we developed an LC mapping ruleset in eCognition Developer 9.2. The general structure is divided into two parts (Fig. 2). Part (a) uses existing ancillary vector data for some LC classes and checks their consistency by comparing them with the EO data. Part (b) updates these classes by adding objects that are not covered by the ancillary vector data and classifies the other classes.

Fig. 2

Structure of the ruleset of the hierarchical LC classification: (a) ancillary vector data processing and (b) classification of areas outside ancillary vector data.

For the sake of transferability, rules are kept as simple as possible using normalized and well-documented indices and criteria. The classification integrates four spectral indices derived from AOP VNIR data: the NDVI,⁴² the NDWI,⁴³ the blue-NDVI (bNDVI),²¹ and the spectral shape index (SSI).¹⁷ The referenced authors reported that bNDVI eases the discrimination between bare soil and asphalt surfaces and that SSI is useful for distinguishing water from shadow.

Part (a) first integrates and second updates the ancillary vector data. First, a coarse chessboard segmentation and class assignment rules integrate ancillary vector data.²³ Second, changes are detected. The removal of buildings is detected using nDSM and NDVI thresholds. NDWI is used to flag potential changes from initial water bodies. The changed areas are then reinjected into the classification process during part (b). Road and rail networks are considered stable in time. They are not updated in the process.

Part (b) classifies areas outside ancillary vector data or identified as changed. An NDVI threshold distinguishes vegetation from artificial areas, grouping pixels of each category into objects. An nDSM threshold subdivides the vegetation into low, medium, and high vegetation objects. The small low and/or medium height vegetation objects surrounded by higher strata are aggregated into medium/high vegetation classes. An SSI index threshold further distinguishes deciduous trees from conifers. A 2.5-m nDSM threshold reclassifies artificial areas into ground and elevated building objects. This threshold value avoids the meaningless detection of low-height features, such as cars on parking lots.⁵⁷ New buildings are detected. Cleaning using shape and context features erases elongated objects caused by the small positional shift between the vector and the EO data. An additional step for the subdivision of the buildings into low and small, low and large, and high buildings is implemented as an indication for the future LU information derivation. A combination of bNDVI, NDWI, SSI thresholds, and contextual criteria reclassifies ground features into asphalt surfaces, bare soils, and new water bodies.

Additionally, the ruleset was adapted to run without ancillary vector data. A classification was performed using this adapted ruleset to assess the gain in accuracy potentially offered by the addition of the different ancillary datasets. The main changes include the use of the NDWI and SSI for the delineation of water bodies at the beginning of the ruleset.

Technically, all tests were run on an i7-4870HG CPU @ 2.50 GHz, 16-GB RAM, 4-cores computer. Processing times were calculated for each test in both the OBIA-rule-based and the OBIA-automated approaches. Application to zone B area was performed using eCognition Server.

3.1.

OBIA-Automated Approach Tested on Zone A

We carried out eight tests to evaluate the performance of the OBIA-automated processing chain and the added value of multisource EO and ancillary vector data (from A to H in Table 2). Varying the input data for the segmentation and the classification steps enabled us to test the added value not only of the AOP versus Pléiades data but also of the NDVI, nDSM, and ancillary vector data integration. The accuracy assessment for the four classifiers and for the SWV is presented in Table 2. Using the SWV, the decision of each classifier is weighted according to its estimated accuracy. Only the SWV is presented as it provides the highest accuracies of all the voting schemes tested in this study.

Table 2

OBIA-automated accuracy assessment: K and OA at the classification level 1 and 2 for the eight tests.

The best accuracy results are achieved for test E in which the AOP, NDVI, and LiDAR nDSM were used as input to both segmentation and classification. OA of 0.83 and 0.92 and K of 0.81 and 0.89 are obtained at level 2 and 1, respectively, using the SWV voting scheme. The classification of buildings achieves good results, with K higher than 0.9. The confusion matrices reveal that the main misclassifications occur between the bare soil and the asphalt surfaces and between the vegetation classes.

The segmentation that integrates multisource EO data generates objects that accurately delineate the true shape of LC elements (test E). It also provides significantly higher accuracies than test D, which combines these data only at the classification step and segments only the VNIR data ($+0.04$ of OA and $+0.05$ of K at level 2 for SWV vote between tests D and E).

Overall, the best-performing classifier is RF. Its performance is closely followed by SVMRadial, which even outperforms RF for both tests that only use VNIR data (A and B). The Rpart and $k$-NN classifiers provide generally significantly lower accuracies and a lower visual quality of the map than RF and SVMRadial. The SWV vote either improves slightly or does not modify the accuracies obtained by individual classifiers.

The OA and K index for all tests are summarized in Fig. 3, which shows the added value of the different input datasets.

Fig. 3

General representation of OA and K index for tests running on AOP with tests (A) only AOP VNIR, (C) AOP VNIR + AOP NDVI, (E) AOP VNIR + AOP NDVI + LiDAR nDSM, (F) AOP VNIR + AOP NDVI + AOP nDSM, and (H) AOP VNIR + AOP NDVI + LiDAR nDSM + ancillary vector data. OA and K for SWV vote at (a) classification level 1 and (b) level 2.

In Fig. 3, to assess the best combination of input data, all tests are compared with test A, which classifies only the AOP multispectral info and reaches an OA of 0.91 and a K index of 0.87 at level 1 and of 0.66 and 0.62, respectively, at level 2 (test A).

By comparing tests C and A, we show that the NDVI allows a gain in accuracy of $+0.05$ for OA and $+0.04$ for K at level 2, which is not significant according to McNemar’s test ($p\text{-}\text{value}=0.1138$). There is also no improvement at level 1.

At level 2, the integration of the LiDAR nDSM (test E) achieves higher OA and K of $+0.14$ and $+0.13$, respectively. It drastically improves the mapping of buildings with a class-specific K index value rising from 0.6 without height information (test C) to 0.9 when LiDAR nDSM is used (test E). Improvements due to the addition of the LiDAR nDSM are statistically significant ($p\text{-}\text{value}<0.001$) compared with the results relying on AOP VNIR + NDVI (test C). Again, the nDSM does not improve the results at level 1. The LiDAR- and the AOP-derived nDSM achieve similar accuracy results. The delineation of elevated features is slightly enhanced using AOP nDSM due to the exact spatial matching between spectral data and height above ground information. Using the LiDAR nDSM as a reference, a $0.47\pm 4.65\text{-}\mathrm{m}$ mean height difference and standard deviation with the AOP nDSM are measured using all validation and training samples. The main height differences are observed for the shadows, the high vegetation, and the water bodies classes. The vegetation growth explains most of the height differences in the vegetated areas. Changes in water levels and artifacts caused by scattering of the LiDAR signal on water surfaces explain the height differences over water bodies. The differences in the shadow class are explained by the small positional shift between both nDSM datasets due to varying camera/plane acquisition angles. Interestingly, the height difference and standard deviation for the buildings class are the smallest of all classes ($0.04\pm 1.47\mathrm{m}$).

As in the OBIA-rule-based approach (see below), the inclusion of ancillary vector data cleans the delineation of objects (test H). However, it does not significantly improve the OA and K values. This means that most misclassifications occur in areas located outside these database features.

The added value of submeter satellite Pléiades data is assessed in comparison with AOP in Table 3 and Fig. 4.

Table 3

Accuracy assessment for both OBIA-rule-based tests: (i) excluding or (ii) including in bold ancillary vector data: K and OA at classification levels 1 and 2.

Fig. 4

Comparison of AOP and Pléiades data: (a) AOP 2012, (b) OBIA-automated using AOP VNIR only (test A, SWV vote), (c) OBIA-automated using AOP VNIR + NDVI and LiDAR nDSM (test E, SWV vote), (d) Pléiades 2013, (e) OBIA-automated using Pléiades VNIR only (test B, SWV vote), and (f) OBIA-automated using Pléiades VNIR + NDVI and LiDAR nDSM (test G, SWV vote). Legend: BU, buildings; AS, asphalt surfaces; BS, bare soils; LV, low vegetation; MV, medium vegetation; HVD, high vegetation deciduous; HVC, high vegetation coniferous; WB, water bodies; and SH, shadows.

Using only VNIR spectral bands, the Pléiades classification (test B) produces OA and K index values $\pm 0.05$ lower at level 2 and $\pm 0.15$ lower at level 1 than the comparable test on the AOP map (test A). Visually, the water bodies and the shadows classes are largely overrepresented in the Pléiades LC map [Fig. 4(e)]. This can be explained by intense sunlight reflection on water bodies leading to confusion with artificial surfaces [Fig. 4(d)]. The shadows also appear less contrasted on the Pléiades imagery than on the AOP. The integration of the LiDAR nDSM significantly improves the accuracies and the visual quality of outputs. It corrects most water misclassifications in test G [Fig. 4(f)]. Accuracy wise (Table 3), higher OAs are still obtained for test E using the AOP in comparison to test G using Pléiades.

3.2.

OBIA-Rule-Based Approach Tested on Zone A

The urban LC mapping results using the OBIA-rule-based approach with and without ancillary vector data are shown in Fig. 5, and the accuracy measures are presented in Table 3.

Fig. 5

Comparison of the two OBIA-rule-based approaches: (a) AOP 2012, (b) OBIA-rule-based approach running on EO data only (without ancillary vector data), and (c) OBIA-rule-based approach running on EO and ancillary vector data. Legend: BU, buildings; AS, asphalt surfaces; BS, bare soils; LV, low vegetation; MV, medium vegetation; HVD, high vegetation deciduous; and HVC, high vegetation coniferous.

Table 3 shows an OA and K index above 0.90 at level 1 and above 0.84 OA and K at level 2. The misclassifications mainly occur between the subclasses at level 2 and between the four vegetation classes. The addition of ancillary vector data improves the OA by $+0.02$ at level 1 and by $+0.03$ at level 2. At classification level 2, such an improvement is statistically significant, with a McNemar $p$-value of 0.02178 (with a significance level of 0.05). This addition mostly improves the classification of asphalt surfaces increasing its specific K by $+0.13$. Interestingly, buildings have a K of 0.97 whether or not ancillary vector data are used. Confusion exists between medium and high vegetation (omission error of 27%) and between deciduous and coniferous vegetation (omission error of 23%).

The addition of ancillary vector data improves the visual quality of the LC map with an enhanced and smoother delineation of objects [Fig. 5(c)]. However, both maps provide valuable information. The LC map produced without ancillary vector data [Fig. 5(b)] is a direct source of information on tree canopy cover. This information may be useful for urban green monitoring. The map produced with ancillary vector data [Fig. 5(c)] keeps information about ground features that are occluded by trees or buildings in the EO data. Therefore, it appears more suitable for GIS application as it eases the link with existing geodatabases. However, constraining the classification using slightly outdated data generates some loss of information. For example, the newly built roundabout is not classified in Fig. 5(c) but well mapped in Fig. 5(b). Both information produced with and without the ancillary data could be computed for inclusion in a database.

Semantically, the OBIA-rule-based approach allows the automatic classification of the shadows using contextual rules. As an example, the shadows detected alongside the buildings were reclassified to the ground-level class (asphalt surface, bare soil, low vegetation, or water) with which they shared the longest border. The approach that does not make use of ancillary vector data does not allow classifying rail network, as rails consist of a complex mix of asphalt surfaces and bare soils.

The visual and statistical analyses of results show a number of mistakes that are not acceptable in an operational context, such as confusion between bridges and buildings or turbulent water surfaces misclassified as asphalt surfaces. Some postprocessing operations will be required to improve the results.

3.3.

Comparison of OBIA Approaches on Zone A

Figure 6 compares the results of both methods with similar input data, i.e., the OBIA-automated approach using the AOP VNIR, NDVI, and LiDAR nDSM data and the OBIA-rule-based approach running without the ancillary vector data. Table 4 details the corresponding confusion matrices. All validation points falling into the shadows were excluded from the OBIA-automated approach accuracy assessment for comparison purposes.

Fig. 6

Comparison of the LC for both OBIA approaches: (a) AOP 2012, (b) OBIA-automated using AOP VNIR + NDVI and LiDAR nDSM (Table 2, test E, SWV vote), and (c) OBIA-rule-based approach running with the EO data without ancillary vector data (Table 3, test i). Legend: BU, buildings; AS, asphalt surfaces; BS, bare soils; LV, low vegetation; MV, medium vegetation; HVD, high vegetation deciduous; HVC, high vegetation coniferous; WB, water bodies; and SH, shadows.

Table 4

Detailed accuracy assessment for both OBIA approaches: (a) OBIA-automated with AOP VNIR + NDVI and nDSM LiDAR (Table 2, test E, SWV vote) and (b) OBIA-rule-based using EO data without ancillary vector data (Table 3, test i): confusion matrices, user’s, producer’s OA, and K at classification level 2.

Visually, the current implementation of the OBIA-rule-based approach provides better results than the OBIA-automated one (Fig. 6). The former already includes shadow reclassification and avoids the overrepresentation of water bodies. It also offers an automated solution for the removal of many small misclassified objects due to contextual rules; these include misclassified urban objects or shadows interpreted as conifers in deciduous forests. Such improvements could be included in the future development of the OBIA-automated approach through postprocessing or additional mapping steps.

Statistically, these visual improvements are reflected by overall higher OA and K index values (Table 4). McNemar’s test shows that they can be considered significant ($p\text{-}\mathrm{value}=0.0028$).

The technical advantages and disadvantages of each method will be developed in Sec. 4. However, processing times have been calculated and can be synthetized as follows: the OBIA-automated approach shows an average processing time of 5-min per ${\mathrm{km}}^2$ running at 0.5-m spatial resolution (Pléiades) and of 20- to 30-min per ${\mathrm{km}}^2$ running at 0.25-m resolution (AOP) using a personal computer. The OBIA-rule-based approach shows an average processing time below 2 min per ${\mathrm{km}}^2$ using ancillary vector data and below 1-min per ${\mathrm{km}}^2$ running without vector data using a personal computer.

3.4.

Application to Zone B

According to Tables 2 and 3, the two best-performing tests in each approach at level 2 are the OBIA-automated test using AOP, NDVI, LiDAR nDSM, and ancillary vector data (see Table 2, test H) and the OBIA-rule-based approach relying on the ancillary vector data (see Table 3, test ii). In this section, these two methods have been applied to the $261\text{-}{\mathrm{km}}^2$ area of Liège. Table 5 details the accuracy assessment for these two tests.

Table 5

Accuracy assessment for the two tests for the Liège area: (a) the OBIA-automated approach using AOP + NDVI + LiDAR nDSM + ancillary vector data and (b) the OBIA-rule-based approach relying on ancillary vector data: K and OA at classification level 1 and 2.

The analysis of Table 5 shows that (1) OA and K values are 4% to 5% higher for the OBIA-automated carried out on zone B rather than on the smaller zone A (see Table 2, test H and Table 5) and (2) OA and K for the OBIA-rule-based approach at level 1 are lower than those obtained for zone A (Table 3, test ii and Table 5). The gain in (1) probably results from the new distribution of training and validation samples. This new distribution improves their representativeness. The decrease in accuracy in (2) is explained by the definition of thresholds. Indeed, the threshold values have been fine-tuned for zone A, and no updating was performed for zone B. The latter covers a larger diversity of LC surfaces that impacts threshold tuning. The differences in results between the two approaches at level 1 and 2 are not statistically significant.

As an illustration, Fig. 7 presents the LC map resulting from the OBIA-rule-based approach with ancillary vector data. Three typical urban neighborhoods of Liège are shown with (a) new isolated or adjacent houses, (b) urban redevelopment in front of the Guillemin rail station in the center of Liège, and (c) industrial and commercial areas expansion in the Sart-Tilman area. The surface statistics per class for the Liège area are presented on the same figure. Impervious surfaces cover 29.3% of the area. At level 2, the most represented LC is low vegetation with 28.8%.

Fig. 7

LC map for the Liège area using the OBIA-rule-based approach with ancillary vector data (1/125,000), surface statistics, and zooms: (a) on a new urban neighborhood in Grâce-Hollogne, (b) on the Guillemin rail station in the center of Liège, and (c) on the extension of the Sart-Tilman industrial and commercial area (1/10,000). Legend: BU, buildings, New BU, new buildings; AS, asphalt surfaces; RA, rail network; LV, low vegetation; MV, medium vegetation; HVD, high vegetation deciduous; HVC, high vegetation coniferous; BS, bare soils; and WB, water bodies.

4.1.

Performance of the OBIA Approaches

We compared two very distinct OBIA solutions for urban LC mapping. First, we developed an OBIA-automated approach that uses algorithms to semiautomatically segment and then classify EO data according to training samples in two separate steps. The manual intervention is limited to choosing segmentation parameters according to i.segment.uspo results and to the development of the sample sets. In agreement with Ref. 58, i.segment.uspo facilitates the tuning of i.segment. The generated objects are of good visual quality. The image is slightly oversegmented, which is usually advised.⁵ Building the training samples is the more time-consuming process. It requires $\sim 6\mathrm{h}$ to generate and to validate the 549 training points. However, sample updating to match the Pléiades data took $<2\mathrm{h}$.

Four algorithms classified the segmented objects. We reported that SVMRadial and RF outperform significantly the $k$-NN and Rpart classifiers in terms of classification accuracy. This confirms the suitability of these widely used algorithms for remote sensing applications.^26,27 We also found that RF outperforms SVMRadial when multisource data are used in the classification. This is in agreement with the concluding remark in Ref. 26. However, no general conclusion can be drawn here as we only tested the radial SVM kernel. Zhang et al.⁵⁹ showed that other SVM kernels, such as RBF and polynomial, could provide higher accuracies than RF. Li et al.⁶⁰ showed that the performance of the classifiers is strongly related to the input datasets. In Ref. 59, $k$-NN was, for example, reported as being as powerful as SVM or RF.

The implemented classification strategy allows the use of voting schemes. In agreement with Ref. 54, we demonstrated the added value of such voting schemes with the SWV vote. This vote showed a more robust performance in general than individual classifiers. Future work could be carried out on the v.class.mIR GRASS GIS add-on to include other classifiers, most notably other SVM kernels,⁶¹ and other voting schemes/combination strategies/classifier ensembles.^28,54,62

Ancillary vector data integration is easy in the OBIA-automated approach, but more developments are needed to match the results achieved in the OBIA-rule-based one. These include the use of contextual rules, such as the postprocessing or the updating of the ancillary vector data. Flexibility regarding the input datasets is an asset of the OBIA-automated approach.

Second, our OBIA-rule-based approach classifies EO data according to a set of expert-defined rules. These rules efficiently use information derived from EO, ancillary vector, contextual, and geometrical characteristics of objects to iteratively classify and reclassify objects until their final class attribution. We confirm the findings by Chen et al.¹⁷ and Dinis et al.²¹ regarding the mapping possibilities offered using NDWI, bNDVI, and SSI. These spectral indices prove efficient for distinguishing water bodies from shadows, asphalt surfaces from bare soils, and deciduous from coniferous forests. However, confusions remain between water bodies and some shadows in dense urban areas. Specific methods, such as presented by Yuan and Sarma,⁶³ should be explored to avoid such confusions.

We also confirm the benefits of using spectral and height information for urban LC mapping.¹⁵ Removing either the spectral or height above ground information will strongly impact the mapping capabilities, such as the number of classes mapped or the resulting classification accuracies. As already demonstrated in Ref. 23, ancillary vector data integration is efficient. The OBIA-rule-based approach also allows an easy update of these databases.

Regarding the transferability of the OBIA-rule-based approach, manual intervention is limited to visual thresholds definition on the four spectral indices used in the ruleset. No training samples are required. Ruleset transferability to other scenes, other periods, or other EO data is not straightforward. Spectral indices behavior and thresholds definition may change between EO data sources and acquisitions. It could indeed be influenced by changes in the period of acquisition that impact, for example, the greenness of vegetation, the shadow contrast level, or the water reflectance values. Data availability could also change. In our case study, if the nDSM information was not available, the development of a completely new ruleset, not relying on height thresholds, would be required. However, we developed this ruleset keeping these limitations in mind. We focused on EO data that are part of Wallonia’s EO acquisition framework that include yearly AOP acquisition and LiDAR data acquisition every 5 years. In the end, our ruleset appears less sophisticated than the ones proposed by O’Neil-Dunne et al.²³ and Van De Kerchove et al.⁵⁶ We strongly believe that this simplification benefits the transferability of the method.

We showed that both OBIA methodologies have pros and cons. Regarding only accuracies and visual quality of the map, it appears to be difficult to recommend one specific approach. However, these are only two preliminary implementations that could be further improved. Additionally, a potential solution is to combine both approaches with first a classification of the LC using trained machine-learning classifiers and then postprocessing using expert-defined rules. This option will be tested in future research.

One of our aims is to provide recommendations toward a suitable method for regional application. This implies analyzing parameters other than just accuracy and map quality. This will be discussed in Sec. 4.3.

4.2.

Added Value of Input Data

We tested the added value of input data in the OBIA-automated approach by comparing the results using VNIR data and by adding (1) NDVI, (2) nDSM, (3) by comparing the AOP and the Pléiades data, and (4) by integrating ancillary vector data.

Initially, we intended to use the NDVI only in the classification. However, the first tests showed that not including the NDVI in the segmentation process generates loss of information for vegetated features. It was then decided to include the NDVI in the segmentation. However, despite a segmentation that looks better according to visual assessment, NDVI integration did not significantly improve the accuracy of the final LC map.

Despite the very low density of points (1 to $3\mathrm{pts}/{\mathrm{m}}^2$) used to generate the LiDAR nDSM, its addition significantly improved the accuracies of the LC map, which is in agreement with Ref. 15. A new acquisition of LiDAR data is currently under negotiation by Walloon authorities. First, elements indicate a planned 7- to $8\text{-}\mathrm{pts}/{\mathrm{m}}^2$ resolution. However, the Walloon EO data acquisition framework will not include yearly coverage of LiDAR data. An acquisition for every 5 years is being discussed. Therefore, as an alternative, we showed that the nDSM generated from AOP provides equivalent results to those obtained with the low-density LiDAR. Producing yearly LC maps benefiting from the added value brought about by the nDSM is thus possible in Wallonia.

We demonstrated that the 0.25-m AOP provides higher accuracies than the 0.5-m Pléiades. The spatial and spectral resolutions and the quality of the data in link with the characteristics of urban features are two parameters that can explain this result. Jensen and Cowen⁶⁴ suggested that the minimum spatial resolution requirement should be one-half the diameter of the smallest object of interest. According to that criterion and to the $15\text{-}{\mathrm{m}}^2$ MMU mapping goal, both EO data seem suitable. However, both of them use pan-sharpening to derive fine-scale XS data. Pan-sharpening often results in spectral distortions. Spectral quality of lower resolution XS data is thus not fully preserved in the fused data. Currently, no satellite sensor can provide a native spatial resolution inferior to 0.5 to 1 m for XS data. Worldview-3 (and soon 4) offers 0.31-m PAN and 1.24-m XS bands.⁶⁵ The lower positional accuracy of Pléiades data is also a source of uncertainty, especially when used in a multisource approach.¹⁴ The improvement of Pléiades’ positional accuracy is currently under discussion with the Pléiades data provider.

We finally assessed the contribution of ancillary vector data integration to the classification. These datasets mostly contribute to a better delineation of objects. A dedicated object-based accuracy assessment could be performed in future research to better assess the quality of the generated objects for cartographic purposes. We also stated the common interest of maps obtained with or without the addition of ancillary vector data for dedicated applications, such as urban green monitoring.

4.3.

Application to the Walloon Region

Operational mapping of the LC at a regional scale, such as Wallonia ($16,844{\mathrm{km}}^2$), generates method- and data-related challenges.

Regarding the methods, automation seems mandatory. Each approach, including a manual step, limits the automation, i.e., thresholding in the OBIA-rule-based one and training in the OBIA-automated approach. On the one hand, thresholding requires similar acquisition dates between the images acquired over the region, which is not realistic in Wallonia. Indeed EO data acquisitions have always been spread across time. For instance, the spectral contrast visible at the bottom of Fig. 1 is due to distinct acquisition periods and thus differing vegetation and illumination status. Thresholds could be defined by processing the data separately according to acquisition date or vegetation status. On the other hand, training samples need to be representative of the LC classes for the whole region and for each acquisition. New solutions for automated training, such as active learning methods,⁶⁶ or the use of open access training databases/frameworks, such as OpenStreetMap, Land Use/Cover Area frame Statistical Survey, or GeoWiki, should be envisaged.

Active learning^66,67 allows the user to begin with only a few training samples. The algorithm then identifies those unlabeled objects that would most improve the classification and asks the user to label them. This leads to higher classification accuracies with lower numbers of training samples. Very recently, a module implementing the core of active learning was uploaded to the GRASS add-ons repository.⁶⁸ We will test the integration of this technique into the processing chain in future works.

Classifying the LC on a large area with submeter multisensor and multisource data poses the challenge of the volume of data to be processed.²³ The AOP data volume for the whole Walloon region is more than 1.5 TB. The Pléiades coverage is lighter than the aerial one with around 60 GB of data. The LiDAR nDSM is more than 50 GB. Enhanced storage capabilities, as well as tiling and automation of the mapping scheme using parallel processing, are required for improved efficiency.²³ Tiling allows limiting temporary memory requirements. Tools for tiling are already available using eCognition Server or GRASS GIS. However, for increasing time efficiency, an implementation of parallel processing is envisaged in future research. Currently, the OBIA-rule-based approach maps the LC at the rate of $2\min /{\mathrm{km}}^2$ on our personal computer. Such a rate means that it would take 23 days to cover the whole of Wallonia, which is problematic. Given the 20- to $30\text{-}\min /{\mathrm{km}}^2$ rate of the OBIA-automated approach, it would take a year to map the LC of Wallonia using EO data at a spatial resolution of 0.25 m.

Several elements of the toolchain already use parallel processing, e.g., the automatic segmentation parameter optimization in i.segment.uspo and the cross validation in v.class.mIR, but the greatest bottleneck is the segmentation step. In future developments, we will, therefore, test the possibility of segmenting tiles in parallel and then combining the resulting objects before classification. An important issue in this approach, however, is the possible border effect. Recent works have attempted to overcome it using irregular cutlines.^69,70 In future work, we will test these approaches and assess the overall balance of gain of speed versus possible loss of accuracy at the tile borders.

Another option would be to lower the spatial resolution. We carried out tests by resampling the AOP data from 0.25 to 0.5, 1, and 2 m (see Sec. 2.3.5 for computer’s specs). The results show that the processing times for applying the processing chain to Wallonia would take 34 days at 0.5-m spatial resolution compared with 360 days at 0.25 m. At 1 and 2 m, the processing time would be further reduced to 11 and 2 days, respectively. Resampling the data to a lower spatial resolution is, therefore, an alternative solution if parallel processing implementation fails to reduce processing times at 0.25 m. The related loss in mapping accuracies and visual quality of the map using lower spatial resolution EO data will then have to be investigated.

Regarding the recommendations for the mapping environment, the open-source solutions have an obvious cost advantage over proprietary software, such as eCognition. The open-source environment also allows the community to propose frequent amendments to the existing codes and even to develop new modules. Proprietary software falls short with respect to research needs in terms of freedoms of use, modification, and distribution of the coded algorithms.⁷¹ However, they often offer proven processing capabilities and, currently, their use is more common within the administration than that of the open-source software. The preference toward one or the other mapping environment will be discussed with the public authorities.

Recommending either AOP or submeter satellite data for mapping the LC is not easy. Data availability, property rights, and acquisition costs should be taken into account in addition to mapping quality and processing capabilities associated with the volume of data to be processed.

First, the Walloon authorities have agreed on an annual acquisition of fully processed AOP. This annual acquisition framework is restricted by high sensitivity of optical airborne and spaceborne sensors to weather conditions. As an example, the 2016 AOP coverage of Wallonia was supposed to be fully acquired during spring. This was not achieved until October due to bad weather conditions. The same argument is applicable to satellite data. As an example, the probability of clear sky has been estimated over the Netherlands, which has a similar climate to Belgium, to only 20%.⁷² As a consequence, data acquisitions to cover large-scale territories are spread across multiple weeks, months, or years with changing LC, such as varying vegetation and illumination conditions.

Second, property rights of satellite images are always owned by the provider while they are fully transferred to the end-user for aerial images.

Third, budgetary constraints, although not the most important, have to be accounted by the administrations for planning their EO data acquisition framework. The current yearly coverage of AOP costs about 12 € per ${\mathrm{km}}^2$, which represents around 200,000 € to cover Wallonia. The cost of the LiDAR acquisition is similar. Acquisition prices for very high-resolution satellite imagery are not publicly available. However, the usual prices range from 3 to 5 to 30 € per ${\mathrm{km}}^2$. Therefore, submeter satellite imagery is not always competitive price-wise. Furthermore, as our acquisition of Pléiades data demonstrated, the framework to obtain preprocessed satellite data is not straightforward and is more costly. However, the waiting time to get access to the data is shorter for satellite imagery as the aerial coverage is usually made available around 6 months to 1 year after the data acquisition in Wallonia.

Future work includes discussing the present results with the Walloon authorities to identify the suitable approach and then further developing the selected classification approach to guarantee its operational functionality and applicability to Wallonia. Also, the present map will be refined to comply with the INSPIRE Pure Land-Cover Component specifications.⁷³ Finally, the LC map will be combined with ancillary socio-economic and thematic geodata to produce an LU map. Both maps will be integrated in a single geodatabase and presented to the authorities. This will help Wallonia to comply with the INSPIRE directive.