be used the other way around to produce several maps from a single one. For instance,
multiple worldwide land-cover products have been obtained in the last decade based on
Landsat and Sentinel imagery with comparable resolutions and nomenclatures. Therefore,
a translation could be used to bring all those products accurately from a single source map
instead of independently reclassifying those image time series. However, as the zero-shot
target procedure still needs to be fully operational, the translation method will not alleviate
the need for existing training samples of each target class.

**Completion** One aims to extend the spatial extent of a target map. We train a model to translate source maps with the desired spatial extent into a target on the target map extent. At inference, the model is used to translate the whole source map and obtain a broad extent target. We compared our results with those obtained by training to classify a single image into the target and showed that translation gives better results than image classification when the target has a minimal spatial extent. On a bigger extent, we showed that the fusion of image and map translation performs significantly better than single-image classification. We attribute this to the fact that translation suffers less from domain adaptation problems, e.g. a "forest" class remains "forest" in the South or North of France while the radiometry and features vary deeply. Thus we believe in the high potential of translation methods for completion. However, we pointed out that translation baselines are more robust in many cases than our machine-learnt solutions. This pledge for more research on improving our method's robustness to spatial generalisation. In particular in the potential of data-augmentation techniques as they are commonly used to tackle generalisation in the image classification field [299]. This would require developing new land-cover-specific data augmentation methods by proposing, for instance, a realistic label-noise augmentation that could be applied to the source land-cover maps.

**Spatial simplification/improvement** One aims to change the resolution of source land-cover maps. Our experiment showed that the method works well for downsampling maps with some minor problems learning very coarse minimum mapping units. Conversely, we did not obtain satisfactory results in upsampling maps. The upsampling without any additional data is ill-defined and thus naturally gives poor results. Our attempts to improve the translation results using highly resolved image data did not provide better results than the image data alone, making the map translation approach obsolete. We point out that no satisfactory solutions have been proposed apart from [202] only targeting a 4-class nomenclature. Inspired by recent advances in image super resolution [34, 350], we believe that generative adversarial networks provide an exciting solution to explore.

**Semantic modifications** One aims to change the nomenclature of a map. This setup is used in all our experimentations and gives satisfactory results. As for now, the translation methods can translate any source nomenclature (even unseen during training) into one of the target nomenclature seen during training. However, we need to provide tools to combine different target nomenclatures efficiently. For instance, one wants to obtain OSO nomenclature with the wetland of CLC and the Open forest of CGLS. As
the method currently outputs the OSO, CLC and CGLS translations, one could define postprocessing rules to merge the different classes. This would face cases where a pixel can be assigned multiple classes. A second, more advanced solution could be to rely on per-class prototypes [282] as they could be combined to derive maps with a unique set of classes while ensuring a unique class for each pixel. A third solution relies on targeting the Semantic representation space directly as the output of the translation framework, as it provides an off-the-shelf method to pick the desired output classes. We point out that quantitatively assessing those solutions' results is difficult as no ground truth data of such recombined nomenclature is available.
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Land cover nomenclatures

<table><caption>Table A.1: OSO nomenclature. The main semantic link column gives for each OSO class the semantically closest class in the other LULC.</caption><thead><tr><th rowspan="2">color</th><th rowspan="2">id</th><th rowspan="2">name</th><th colspan="5">Main semantic link</th></tr><tr><th>C</th><th>P</th><th>G1</th><th>G2</th><th>M</th></tr></thead><tbody><tr><td></td><td>1</td><td>Continuous urban fabric</td><td>111</td><td>9</td><td>1111</td><td>235</td><td>7</td></tr><tr><td></td><td>2</td><td>Discontinuous urban fabric</td><td>112</td><td>9</td><td>1111</td><td>235</td><td>6</td></tr><tr><td></td><td>3</td><td>Industrial or commercial units</td><td>121</td><td>9</td><td>1111</td><td>235</td><td>8</td></tr><tr><td></td><td>4</td><td>Road surfaces</td><td>122</td><td>9</td><td>1112</td><td>411</td><td>10</td></tr><tr><td></td><td>5</td><td>rapeseeds</td><td>211</td><td>8</td><td>221</td><td>11</td><td>3</td></tr><tr><td></td><td>6</td><td>cereals</td><td>211</td><td>8</td><td>221</td><td>11</td><td>3</td></tr><tr><td></td><td>7</td><td>protein crops</td><td>211</td><td>8</td><td>221</td><td>11</td><td>3</td></tr><tr><td></td><td>8</td><td>soy</td><td>211</td><td>8</td><td>221</td><td>11</td><td>3</td></tr><tr><td></td><td>9</td><td>sunflower</td><td>211</td><td>8</td><td>221</td><td>11</td><td>3</td></tr><tr><td></td><td>10</td><td>maize</td><td>211</td><td>8</td><td>221</td><td>11</td><td>3</td></tr><tr><td></td><td>11</td><td>rice</td><td>211</td><td>8</td><td>221</td><td>11</td><td>3</td></tr><tr><td></td><td>12</td><td>tubers</td><td>211</td><td>8</td><td>221</td><td>11</td><td>3</td></tr><tr><td></td><td>13</td><td>Intensive grassland</td><td>231</td><td>4</td><td>221</td><td>11</td><td>3</td></tr><tr><td></td><td>14</td><td>Orchards</td><td>222</td><td>3</td><td>2111</td><td>11</td><td>3</td></tr><tr><td></td><td>15</td><td>Vineyards</td><td>221</td><td>2</td><td>213</td><td>11</td><td>3</td></tr><tr><td></td><td>16</td><td>Broad-leaved forest</td><td>311</td><td>1</td><td>2111</td><td>12</td><td>1</td></tr><tr><td></td><td>17</td><td>Coniferous forest</td><td>312</td><td>1</td><td>2112</td><td>12</td><td>1</td></tr><tr><td></td><td>18</td><td>Natural grasslands</td><td>321</td><td>4</td><td>221</td><td>63</td><td>2</td></tr><tr><td></td><td>19</td><td>Woody moorlands</td><td>324</td><td>3</td><td>212</td><td>63</td><td>2</td></tr><tr><td></td><td>20</td><td>Bare rock</td><td>332</td><td>7</td><td>121</td><td>63</td><td>2</td></tr><tr><td></td><td>21</td><td>Beaches, dunes and sand plains</td><td>331</td><td>7</td><td>1121</td><td>63</td><td>2</td></tr><tr><td></td><td>22</td><td>Glaciers and perpetual snow</td><td>335</td><td>10</td><td>123</td><td>63</td><td>2</td></tr><tr><td></td><td>23</td><td>Water bodies</td><td>523</td><td>12</td><td>122</td><td>14</td><td>4</td></tr></tbody></table>
<table><caption>Table A.2: CLC nomenclature. The main semantic link column gives for each CLC class the semantically closest class in the other LULC.</caption><thead><tr><th rowspan="2">color</th><th rowspan="2">id</th><th rowspan="2">name</th><th colspan="5">Main semantic link</th></tr><tr><th>P</th><th>O</th><th>G1</th><th>G2</th><th>M</th></tr></thead><tbody><tr><td></td><td>111</td><td>Continuous urban fabric</td><td>9</td><td>1</td><td>1111</td><td>235</td><td>7</td></tr><tr><td></td><td>112</td><td>Discontinuous urban fabric</td><td>9</td><td>2</td><td>1111</td><td>235</td><td>6</td></tr><tr><td></td><td>121</td><td>Industrial or commercial units</td><td>9</td><td>3</td><td>1111</td><td>235</td><td>8</td></tr><tr><td></td><td>122</td><td>Road and rail networks and associated land</td><td>9</td><td>4</td><td>1112</td><td>411</td><td>10</td></tr><tr><td></td><td>123</td><td>Port areas</td><td>9</td><td>3</td><td>1112</td><td>414</td><td>8</td></tr><tr><td></td><td>124</td><td>Airports</td><td>9</td><td>3</td><td>1112</td><td>413</td><td>10</td></tr><tr><td></td><td>131</td><td>Mineral extraction sites</td><td>7</td><td>20</td><td>1121</td><td>13</td><td>11</td></tr><tr><td></td><td>132</td><td>Dump sites</td><td>9</td><td>20</td><td>1122</td><td>43</td><td>11</td></tr><tr><td></td><td>133</td><td>Construction sites</td><td>9</td><td>20</td><td>1121</td><td>61</td><td>11</td></tr><tr><td></td><td>141</td><td>Green urban areas</td><td>4</td><td>13</td><td>221</td><td>235</td><td>5</td></tr><tr><td></td><td>142</td><td>Sport and leisure facilities</td><td>9</td><td>3</td><td>1111</td><td>235</td><td>5</td></tr><tr><td></td><td>211</td><td>Non-irrigated arable land</td><td>8</td><td>6</td><td>221</td><td>11</td><td>3</td></tr><tr><td></td><td>212</td><td>Permanently irrigated land</td><td>8</td><td>13</td><td>221</td><td>11</td><td>3</td></tr><tr><td></td><td>213</td><td>Rice fields</td><td>8</td><td>11</td><td>221</td><td>11</td><td>3</td></tr><tr><td></td><td>221</td><td>Vineyards</td><td>3</td><td>15</td><td>213</td><td>11</td><td>3</td></tr><tr><td></td><td>222</td><td>Fruit trees and berry plantations</td><td>2</td><td>14</td><td>2111</td><td>11</td><td>3</td></tr><tr><td></td><td>223</td><td>Olive groves</td><td>2</td><td>14</td><td>2111</td><td>11</td><td>3</td></tr><tr><td></td><td>231</td><td>Pastures</td><td>4</td><td>13</td><td>221</td><td>11</td><td>3</td></tr><tr><td></td><td>241</td><td>Annual crops associated with permanent crops</td><td>8</td><td>13</td><td>221</td><td>11</td><td>3</td></tr><tr><td></td><td>242</td><td>Complex cultivation patterns</td><td>8</td><td>14</td><td>221</td><td>11</td><td>3</td></tr><tr><td></td><td>243</td><td>Mainly agriculture but significant areas of natural vegetation</td><td>8</td><td>14</td><td>2111</td><td>12</td><td>3</td></tr><tr><td></td><td>244</td><td>Agro-forestry areas</td><td>1</td><td>16</td><td>2111</td><td>12</td><td>1</td></tr><tr><td></td><td>311</td><td>Broad-leaved forest</td><td>1</td><td>16</td><td>2111</td><td>12</td><td>1</td></tr><tr><td></td><td>312</td><td>Coniferous forest</td><td>1</td><td>17</td><td>2112</td><td>12</td><td>1</td></tr><tr><td></td><td>313</td><td>Mixed forest</td><td>1</td><td>17</td><td>2113</td><td>12</td><td>1</td></tr><tr><td></td><td>321</td><td>Natural grassland</td><td>4</td><td>18</td><td>221</td><td>63</td><td>2</td></tr><tr><td></td><td>322</td><td>Moors and heathland</td><td>3</td><td>19</td><td>212</td><td>63</td><td>2</td></tr><tr><td></td><td>323</td><td>Sclerophyllous vegetation</td><td>3</td><td>19</td><td>212</td><td>63</td><td>1</td></tr><tr><td></td><td>324</td><td>Transitional woodland/shrub</td><td>3</td><td>19</td><td>212</td><td>12</td><td>1</td></tr><tr><td></td><td>331</td><td>Beaches, dunes, sands</td><td>7</td><td>21</td><td>121</td><td>63</td><td>2</td></tr><tr><td></td><td>332</td><td>Bare rock</td><td>7</td><td>20</td><td>121</td><td>63</td><td>2</td></tr><tr><td></td><td>333</td><td>Sparsely vegetated areas</td><td>7</td><td>18</td><td>221</td><td>63</td><td>2</td></tr><tr><td></td><td>334</td><td>Burnt areas</td><td>3</td><td>19</td><td>212</td><td>63</td><td>2</td></tr><tr><td></td><td>335</td><td>Glaciers and perpetual snow</td><td>10</td><td>22</td><td>123</td><td>63</td><td>2</td></tr><tr><td></td><td>411</td><td>Inland marshes</td><td>5</td><td>19</td><td>212</td><td>11</td><td>2</td></tr><tr><td></td><td>412</td><td>Peatbogs</td><td>5</td><td>19</td><td>212</td><td>11</td><td>2</td></tr><tr><td></td><td>421</td><td>Salt marshes</td><td>7</td><td>21</td><td>1121</td><td>63</td><td>2</td></tr><tr><td></td><td>422</td><td>Salines</td><td>11</td><td>21</td><td>1121</td><td>13</td><td>11</td></tr><tr><td></td><td>423</td><td>Intertidal flats</td><td>11</td><td>21</td><td>1121</td><td>63</td><td>2</td></tr><tr><td></td><td>511</td><td>Water courses</td><td>11</td><td>23</td><td>122</td><td>63</td><td>4</td></tr><tr><td></td><td>512</td><td>Water bodies</td><td>11</td><td>23</td><td>122</td><td>63</td><td>4</td></tr><tr><td></td><td>521</td><td>Coastal lagoons</td><td>11</td><td>23</td><td>122</td><td>63</td><td>4</td></tr><tr><td></td><td>522</td><td>Estuaries</td><td>12</td><td>23</td><td>122</td><td>63</td><td>4</td></tr><tr><td></td><td>523</td><td>Sea and ocean</td><td>12</td><td>23</td><td>122</td><td>63</td><td>4</td></tr></tbody></table>
<table><caption>Table A.3: MOS nomenclature. The main semantic link column gives for each MOS class the semantically closest class in the other LULC.</caption><thead><tr><th rowspan="2">color</th><th rowspan="2">id</th><th rowspan="2">name</th><th colspan="5">Main semantic link</th></tr><tr><th>C</th><th>P</th><th>O</th><th>G1</th><th>G2</th></tr></thead><tbody><tr><td></td><td>1</td><td>Forest</td><td>311</td><td>1</td><td>16</td><td>2111</td><td>12</td></tr><tr><td></td><td>2</td><td>Semi-natural areas</td><td>321</td><td>3</td><td>18</td><td>221</td><td>11</td></tr><tr><td></td><td>3</td><td>Crops</td><td>211</td><td>8</td><td>6</td><td>221</td><td>11</td></tr><tr><td></td><td>4</td><td>Water</td><td>511</td><td>11</td><td>23</td><td>122</td><td>414</td></tr><tr><td></td><td>5</td><td>Artificialized green urban areas</td><td>142</td><td>4</td><td>2</td><td>221</td><td>235</td></tr><tr><td></td><td>6</td><td>Individual housing</td><td>112</td><td>9</td><td>2</td><td>1111</td><td>235</td></tr><tr><td></td><td>7</td><td>Colective housing</td><td>111</td><td>9</td><td>1</td><td>1111</td><td>235</td></tr><tr><td></td><td>8</td><td>Activities</td><td>121</td><td>9</td><td>3</td><td>1111</td><td>235</td></tr><tr><td></td><td>9</td><td>Facilities</td><td>111</td><td>9</td><td>2</td><td>1111</td><td>235</td></tr><tr><td></td><td>10</td><td>Transports</td><td>122</td><td>9</td><td>4</td><td>1112</td><td>411</td></tr><tr><td></td><td>11</td><td>Mine/dump/construction</td><td>131</td><td>9</td><td>3</td><td>1121</td><td>13</td></tr></tbody></table>

<table><caption>Table A.4: OCS-GEc nomenclature. The main semantic link column gives for each OCS-GEc class the semantically closest class in the other LULC.</caption><thead><tr><th rowspan="2">color</th><th rowspan="2">id</th><th rowspan="2">name</th><th colspan="5">Main semantic link</th></tr><tr><th>C</th><th>P</th><th>O</th><th>G2</th><th>M</th></tr></thead><tbody><tr><td></td><td>1111</td><td>Built-up areas</td><td>111</td><td>9</td><td>1</td><td>235</td><td>6</td></tr><tr><td></td><td>1112</td><td>Undeveloped areas</td><td>122</td><td>9</td><td>4</td><td>411</td><td>10</td></tr><tr><td></td><td>1121</td><td>Mineral material areas</td><td>131</td><td>9</td><td>21</td><td>412</td><td>11</td></tr><tr><td></td><td>1122</td><td>Areas with other composite materials</td><td>132</td><td>9</td><td>3</td><td>43</td><td>11</td></tr><tr><td></td><td>121</td><td>Bare soils</td><td>332</td><td>7</td><td>20</td><td>63</td><td>2</td></tr><tr><td></td><td>122</td><td>Water surfaces</td><td>512</td><td>11</td><td>23</td><td>414</td><td>4</td></tr><tr><td></td><td>123</td><td>Snowfields and glaciers</td><td>335</td><td>10</td><td>22</td><td>63</td><td>2</td></tr><tr><td></td><td>2111</td><td>Deciduous stands</td><td>311</td><td>1</td><td>16</td><td>12</td><td>1</td></tr><tr><td></td><td>2112</td><td>Conifer stands</td><td>312</td><td>1</td><td>17</td><td>12</td><td>1</td></tr><tr><td></td><td>2113</td><td>Mixed stands</td><td>313</td><td>1</td><td>16</td><td>12</td><td>1</td></tr><tr><td></td><td>212</td><td>Shrub and sub-shrub formations</td><td>324</td><td>3</td><td>19</td><td>63</td><td>1</td></tr><tr><td></td><td>213</td><td>Other woody formations</td><td>221</td><td>3</td><td>15</td><td>11</td><td>3</td></tr><tr><td></td><td>221</td><td>Herbaceous formations</td><td>211</td><td>8</td><td>6</td><td>11</td><td>3</td></tr><tr><td></td><td>222</td><td>Other non-woody formations</td><td>334</td><td>4</td><td>18</td><td>63</td><td>2</td></tr></tbody></table>
<table><caption>Table A.5: OCS-GEu nomenclature. The main semantic link column gives for each OCS-GEu class the semantically closest class in the other LULC.</caption><thead><tr><th rowspan="2">color</th><th rowspan="2">id</th><th rowspan="2">name</th><th colspan="5">Main semantic link</th></tr><tr><th>C</th><th>P</th><th>O</th><th>G1</th><th>M</th></tr></thead><tbody><tr><td></td><td>11</td><td>Agriculture</td><td>211</td><td>8</td><td>6</td><td>221</td><td>3</td></tr><tr><td></td><td>12</td><td>Forestry</td><td>311</td><td>1</td><td>16</td><td>2111</td><td>1</td></tr><tr><td></td><td>13</td><td>Extraction activities</td><td>131</td><td>7</td><td>20</td><td>1121</td><td>11</td></tr><tr><td></td><td>14</td><td>Fisheries and aquaculture</td><td>521</td><td>11</td><td>23</td><td>122</td><td>4</td></tr><tr><td></td><td>235</td><td>Secondary or tertiary production and residential usage</td><td>112</td><td>9</td><td>2</td><td>1111</td><td>6</td></tr><tr><td></td><td>411</td><td>Road networks</td><td>122</td><td>9</td><td>4</td><td>1112</td><td>10</td></tr><tr><td></td><td>412</td><td>Rails networks</td><td>122</td><td>9</td><td>4</td><td>1121</td><td>10</td></tr><tr><td></td><td>413</td><td>Overhead networks</td><td>124</td><td>9</td><td>23</td><td>1112</td><td>10</td></tr><tr><td></td><td>414</td><td>River and maritime transport networks</td><td>123</td><td>12</td><td>3</td><td>122</td><td>10</td></tr><tr><td></td><td>42</td><td>Logistics and storage services</td><td>121</td><td>9</td><td>3</td><td>1111</td><td>8</td></tr><tr><td></td><td>43</td><td>Public utility networks</td><td>121</td><td>9</td><td>3</td><td>1111</td><td>8</td></tr><tr><td></td><td>61</td><td>Transitionnal Areas</td><td>133</td><td>9</td><td>3</td><td>1121</td><td>11</td></tr><tr><td></td><td>62</td><td>Abandoned areas</td><td>322</td><td>2</td><td>3</td><td>212</td><td>11</td></tr><tr><td></td><td>63</td><td>Without use</td><td>321</td><td>2</td><td>18</td><td>212</td><td>2</td></tr></tbody></table>

<table><caption>Table A.6: CGLS-LC100 nomenclature. The main semantic link column gives for each CGLS-LC100 class the semantically closest class in the other LULC.</caption><thead><tr><th rowspan="2">color</th><th rowspan="2">id</th><th rowspan="2">name</th><th colspan="5">Main semantic link</th></tr><tr><th>C</th><th>O</th><th>G1</th><th>G2</th><th>M</th></tr></thead><tbody><tr><td></td><td>11</td><td>Closed forest</td><td>311</td><td>16</td><td>2111</td><td>12</td><td>1</td></tr><tr><td></td><td>12</td><td>Open forest</td><td>231</td><td>16</td><td>212</td><td>12</td><td>1</td></tr><tr><td></td><td>20</td><td>Shrubland</td><td>221</td><td>15</td><td>213</td><td>11</td><td>3</td></tr><tr><td></td><td>30</td><td>Herbaceous vegetation</td><td>321</td><td>13</td><td>221</td><td>11</td><td>3</td></tr><tr><td></td><td>90</td><td>Herbaceous wetland</td><td>411</td><td>23</td><td>122</td><td>63</td><td>4</td></tr><tr><td></td><td>100</td><td>Moss and lichen</td><td>333</td><td>20</td><td>222</td><td>63</td><td>2</td></tr><tr><td></td><td>60</td><td>Bare / sparse vegetation</td><td>332</td><td>20</td><td>121</td><td>63</td><td>2</td></tr><tr><td></td><td>40</td><td>Cropland</td><td>211</td><td>6</td><td>221</td><td>11</td><td>3</td></tr><tr><td></td><td>50</td><td>Built-up</td><td>112</td><td>2</td><td>1111</td><td>235</td><td>6</td></tr><tr><td></td><td>70</td><td>Snow and ice</td><td>335</td><td>22</td><td>123</td><td>63</td><td>2</td></tr><tr><td></td><td>80</td><td>Permanent water bodies</td><td>512</td><td>23</td><td>122</td><td>14</td><td>4</td></tr><tr><td></td><td>200</td><td>Ocean</td><td>523</td><td>23</td><td>122</td><td>414</td><td>4</td></tr></tbody></table>
Semantic correspondance analysis between maps
of the MLULC dataset

The following figure presents the proposed handcarfted semantic translation from source (in row) to target in column for all couple of source/target maps. Blue square indicates strong semantic correspondance. The codes of classes are provided in appendix. For instance, in Figure B.1 the first row presents the possible semantic association for the CGLS class 1 (Closed Forest) which establish three semantic semantic relation with CLC class 22,23,24,25 (Agro-Forestry, Broad-leaved Forest, Coniferous Forest, Mixed Forest). Note that some possible semantic translations are not represented as they are perceived as weaker. For instance, it could also be translated in CLC class 21 (Land principally occupied by agriculture, with significant areas of natural vegetation) but the semantic link is weaker as class 21 includes other stuffs than Forest.

<figure><img src="image_75.png" /><figcaption>Figure B.1: Semantic translation from CGLS-LC100 (in row) to CLC (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>
<figure><img src="image_76.png" /><figcaption>Figure B.2: Semantic translation from CGLS-LC100 (in row) to OSO (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>
<figure><img src="image_77.png" /><figcaption>Figure B.3: Semantic translation from CGLS-LC100 (in row) to OCSGE-cover (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>
<figure><img src="image_78.png" /><figcaption>Figure B.4: Semantic translation from CGLS-LC100 (in row) to OCSGE-use (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>
<figure><img src="image_79.png" /><figcaption>Figure B.5: Semantic translation from CGLS-LC100 (in row) to MOS (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>
<figure><img src="image_80.png" /><figcaption>Figure B.6: Semantic translation from CLC (in row) to CGLS-LC100 (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>
<figure><img src="image_81.png" /><figcaption>Figure B.7: Semantic translation from CLC (in row) to OSO (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>
<figure><img src="image_82.png" /><figcaption>Figure B.8: Semantic translation from CLC (in row) to OCSGE-cover (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>
<figure><img src="image_83.png" /><figcaption>Figure B.9: Semantic translation from CLC (in row) to OCSGE-use (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>
<figure><img src="image_84.png" /><figcaption>Figure B.10: Semantic translation from CLC (in row) to MOS (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>
<figure><img src="image_85.png" /><figcaption>Figure B.11: Semantic translation from OSO (in row) to CGLS-LC100 (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>
<figure><img src="image_86.png" /><figcaption>Figure B.12: Semantic translation from OSO (in row) to CLC (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>
<figure><img src="image_87.png" /><figcaption>Figure B.13: Semantic translation from OSO (in row) to OCSGE-cover (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>
<figure><img src="image_88.png" /><figcaption>Figure B.14: Semantic translation from OSO (in row) to OCSGE-use (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>
<figure><img src="image_89.png" /><figcaption>Figure B.15: Semantic translation from OSO (in row) to MOS (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>
<figure><img src="image_90.png" /><figcaption>Figure B.16: Semantic translation from OCSGE-cover (in row) to CGLS-LC100 (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>
<figure><img src="image_91.png" /><figcaption>Figure B.17: Semantic translation from OCSGE-cover (in row) to CLC (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>

<figure><img src="image_92.png" /><figcaption>Figure B.18: Semantic translation from OCSGE-cover (in row) to OSO (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>
<figure><img src="image_93.png" /><figcaption>Figure B.19: Semantic translation from OCSGE-cover (in row) to OCSGE-use (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>
<figure><img src="image_94.png" /><figcaption>Figure B.20: Semantic translation from OCSGE-cover (in row) to MOS (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>
<figure><img src="image_95.png" /><figcaption>Figure B.21: Semantic translation from OCSGE-use (in row) to CGLS-LC100 (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>
<figure><img src="image_96.png" /><figcaption>Figure B.22: Semantic translation from OCSGE-use (in row) to CLC (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>

<figure><img src="image_97.png" /><figcaption>Figure B.23: Semantic translation from OCSGE-use (in row) to OSO (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>
<figure><img src="image_98.png" /><figcaption>Figure B.24: Semantic translation from OCSGE-use (in row) to OCSGE-cover (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>
<figure><img src="image_99.png" /><figcaption>Figure B.25: Semantic translation from OCSGE-use (in row) to MOS (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>
<figure><img src="image_100.png" /><figcaption>Figure B.26: Semantic translation from MOS (in row) to CGLS-LC100 (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>

<figure><img src="image_101.png" /><figcaption>Figure B.27: Semantic translation from MOS (in row) to CLC (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>
<figure><img src="image_102.png" /><figcaption>Figure B.28: Semantic translation from MOS (in row) to OSO (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>
<figure><img src="image_103.png" /><figcaption>Figure B.29: Semantic translation from MOS (in row) to OCSGE-cover (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>
<figure><img src="image_104.png" /><figcaption>Figure B.30: Semantic translation from MOS (in row) to OCSGE-use (in column). For more explanation on how to read the Figure refer to the presentation of Appendix B.</figcaption></figure>
Semantic vs statistic translation

Table C.1 gives the proportion of target classes that can be obtained from the translation of one single source to one single target (1-to-1), one source into multiple target (1-to-n) and no source class corresponds to the target (0-to-1) estimated using the matrices in Appendix B. High 1-to-1 values denote easy translation as all pixels with a class establishing a 1-to-1 translation shall be translated the same-way. High 1-to-n values denote complicated translation as pixels with the same class shall be translated into multiple targets which requires context analysis or additional data. High 0-to-1 denotes ill-defined translations in which multiple target class have no clear source class correspondent. We estimate those proportion using different approaches, symmetric and asymmetric. The asymmetric method only considers the source to target translation matrix while the symmetric one combines the source to target with the transposed target to source translation matrix. This asymmetric translation considers that a source class can not only be translated into its main semantic correspondents (asymmetric) but also in all target classes that can be translated into the source. For instance, OSO Wheat crops have a clear main semantic correspondent in the CLC nomenclature: *Non-irrigated arable crops*, thus all Wheat crops shall be translated into *Non-irrigated arable crops* in a "1-to-1" way. However, this results in the fact that classes such as *Mainly agriculture but significant areas of natural vegetation* which might partially include Wheat crops have no corresponding source class in the OSO to CLC translation matrix resulting in a high "0-to-1" proportion. Conversely, when one considers the reverse CLC to OSO translation both *Non-irrigated arable crops* and *Mainly agriculture but significant areas of natural vegetation* can both be translated into OSO Wheat crops. In practice this implies that the asymmetric (symmetric) measurement tends to underestimate (overestimate) the proportion of 1-to-n translation and overestimate (underestimate) 1-to-1 and 0-to-n translations. Therefore one should consider that the correct proportion of classes establishing each of the configuration is comprised between the symmetric and asymmetric values.
<table><caption>Table C.1: Estimation of the proportion of target classes obtainable from translation where one source class is translated into a single target class (1-to-1), into multiple target classes (1-to-n), or where no source classes is found for the target (0-to-1) for each source and target maps of the MLULC dataset. 2 different estimation technique are used. Symmetric use the source to target and the reverse target to source translation matrix (given in Appendix B) to estimate all possible translation from each source class to each target class, i.e. possible source to target class translation also includes target classes that are translated into source. Asymmetric only considers the source to target translation matrix.</caption><thead><tr><th rowspan="2">source<br>target</th><th colspan="4">CGLS</th><th colspan="4">CLC</th><th colspan="4">OSO</th><th colspan="4">OCSGE</th><th colspan="4">OCSGE</th><th colspan="4">MOS</th><th rowspan="2">Average</th></tr><tr><th>CLC</th><th>OSO</th><th>OCSGE</th><th>OCSGEa</th><th>MOS</th><th>CLC</th><th>OSO</th><th>OCSGE</th><th>OCSGEa</th><th>MOS</th><th>CLC</th><th>OSO</th><th>OCSGE</th><th>OCSGEa</th><th>MOS</th><th>CLC</th><th>OSO</th><th>OCSGE</th><th>OCSGEa</th><th>MOS</th><th>CLC</th><th>OSO</th><th>OCSGE</th><th>OCSGEa</th></tr></thead><tbody><tr><td rowspan="2">Asymmetric</td><td>1-to-1 [%]</td><td>4</td><td>13</td><td>35</td><td>14</td><td>36</td><td>58</td><td>65</td><td>78</td><td>57</td><td>63</td><td>59</td><td>34</td><td>42</td><td>28</td><td>45</td><td>41</td><td>11</td><td>20</td><td>21</td><td>54</td><td>9</td><td>6</td><td>4</td><td>21</td><td>25</td><td>8</td><td>4</td><td>17</td><td>14</td><td>25</td><td>31</td></tr><tr><td>0-to-1 [%]</td><td>71</td><td>83</td><td>58</td><td>58</td><td>55</td><td>14</td><td>35</td><td>22</td><td>36</td><td>37</td><td>34</td><td>16</td><td>58</td><td>30</td><td>46</td><td>59</td><td>62</td><td>74</td><td>65</td><td>37</td><td>92</td><td>94</td><td>96</td><td>79</td><td>55</td><td>92</td><td>94</td><td>83</td><td>79</td><td>65</td><td>60</td></tr><tr><td rowspan="2">Symmetric</td><td>1-to-1 [%]</td><td>25</td><td>4</td><td>7</td><td>28</td><td>9</td><td>8</td><td>0</td><td>0</td><td>7</td><td>8</td><td>16</td><td>50</td><td>0</td><td>42</td><td>9</td><td>0</td><td>27</td><td>0</td><td>14</td><td>9</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2</td><td>0</td><td>0</td><td>7</td><td>7</td><td>7</td><td>23</td></tr><tr><td>0-to-1 [%]</td><td>4</td><td>8</td><td>35</td><td>14</td><td>36</td><td>58</td><td>65</td><td>78</td><td>29</td><td>63</td><td>33</td><td>2</td><td>35</td><td>14</td><td>27</td><td>41</td><td>4</td><td>17</td><td>7</td><td>36</td><td>8</td><td>2</td><td>4</td><td>14</td><td>45</td><td>8</td><td>2</td><td>17</td><td>14</td><td>21</td><td>72</td></tr><tr><td></td><td></td><td>71</td><td>92</td><td>58</td><td>72</td><td>55</td><td>14</td><td>35</td><td>22</td><td>72</td><td>17</td><td>67</td><td>98</td><td>65</td><td>86</td><td>64</td><td>59</td><td>87</td><td>83</td><td>86</td><td>64</td><td>92</td><td>98</td><td>96</td><td>86</td><td>55</td><td>92</td><td>96</td><td>83</td><td>79</td><td>79</td><td>3</td></tr><tr><td></td><td></td><td>25</td><td>0</td><td>7</td><td>14</td><td>9</td><td>8</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>7</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2</td><td>0</td><td>0</td><td>7</td><td>0</td><td>0</td></tr></tbody></table>
Class definition
<table><caption>Table D.1: CLC Class definition</caption><thead><tr><th>label</th><th>title</th><th>text</th></tr></thead><tbody><tr><td>1</td><td>Continuous urban fabric</td><td>The continuous urban fabric class is assigned when urban structures and transport networks are dominating the surface area. More than 80% of the land surface is covered by impermeable features like buildings, roads and artificially surfaced areas. The discontinuous urban fabric class is assigned when urban structures and transport networks associated with vegetated areas and bare surfaces are present and occupy significant surfaces in a discontinuous spatial pattern. The impermeable features like buildings, roads and artificially surfaced areas range from 30 to 80% land coverage.<br>Buildings, other built-up structures and artificial surfaces (with concrete, asphalt, tarmacroad, or stabilised life e.g. heated earth) occupy most of the area. It can also contain vegetation (mostly grass) or other non-sealed surfaces. This class is assigned for land units that are under industrial or commercial use or serve for public service facilities.</td></tr><tr><td>2</td><td>Discontinuous urban fabric</td><td>Motorways and railways, including associated installations.<br>Infrastructure of port areas (land and water surface), including quays, dockyards and marinas.<br>Airports.<br>Open-pit extraction sites of construction materials (sandstones, quarries) or other minerals (open-cast mines). Includes flooded mining pits.<br>Public industrial or mine dump sites. Public industrial or mine dump sites.<br>Space under construction development, soil or bedrock excavations, earthworks. This class is assigned for areas where landscape is affected by human activities, changed or modified into artificial surfaces, being in a state of antropogenic transition.<br>Areas with vegetation within or partly covered by urban fabric. This class is assigned for urban greenery, which usually has recreational or environmental character and is usually accessible for the public.<br>This class is assigned for areas used for sports, leisure and recreation purposes. Camping grounds, sports grounds, leisure parks, golf courses, racetraces etc. belong to this class, as well as formal parks not surrounded by urban areas.<br>Cultivated land parcels under a marked agricultural use for annually harvested non-permanent crops, normally under a crop rotation system, including fallow lands within such crop rotation. Fields withспоарынапынинание с нанепер permanent devices to support dominant rainfed cultivation are included.<br>Cultivated land parcels under agricultural use for arable crops that are permanently or periodically irrigated, using a permanent infrastructure (irrigation channels, drainage network and additional irrigation facilities). Most of these crops cannot be cultivated without artificial water supply. Does not include sporadically irrigated land.</td></tr><tr><td>14</td><td>Rice fields</td><td>Cultivated land parcels prepared for rice production, consisting of periodically flooded flat surfaces with irrigation channels.</td></tr><tr><td>15</td><td>Vineyards</td><td>Areas planted with vines, vineyard parcels covering 50% and determining the land use of the area.</td></tr><tr><td>16</td><td>Fruit tree and berry plantations</td><td>Cultivated parcels planted with fruit trees and shrubs, intended for fruit production, including nuts. The planting pattern can be by single or mixed fruit species, both in association with permanently grassy surfaces.</td></tr><tr><td>17</td><td>Olive groves</td><td>Cultivated areas planted with olive trees.</td></tr><tr><td>18</td><td>Pastures, meadows and other permanent grasslands under agricultural use</td><td>Permanent grassland characterized by agricultural use or strong human disturbance. Floral composition dominated by granimaceae and influenced by human activity. Typically used for grazing pastures or mechanical harvesting of grass meadows.<br>Cultivated land parcels with non-permanent crops (mostly arable land) associated with permanent crops (fruit trees or olive trees or vines) on the same parcel.</td></tr><tr><td>19</td><td>Annual crops associated with permanent crops</td><td>Mosaics of small cultivated land parcels with different cultivation types: annual crops, pasture and or permanent crops - eventually with scattered houses or gardens.<br>Areas principally occupied by agriculture, interspersed with significant natural or semi-natural areas (including forests, shrubs, wetlands, water bodies, mineral outcrops) in a mosaic pattern.</td></tr><tr><td>20</td><td>Complex cultivation patterns</td><td>Annual crops or greening land under the wooded cover of forestry species.<br>Vegigation formation composed principally of trees, including shrub and bush understory, where broad-leaved species predominate.<br>Vegigation formation composed principally of trees, including shrub and bush understory, where coniferous species predominate.<br>Vegigation formation composed principally of trees, including shrub and bush understory, where neither broad-leaved nor coniferous species predominate.<br>Grasslands under no or moderate human influence. Low productivity grasslands. Often situated in areas of rough, uneven ground, steep slopes, frequently including rocky areas or patches of other (semi-natural vegetation.<br>Vegigation with low and closed cover, dominated by bushes, shrubs, dwarf shrubs (broadleaf, briars, broom, gorse, labrumum etc.) and herbaceous plants, forming a climax stage of development.</td></tr><tr><td>21</td><td>Land principally occupied by agriculture, with significant areas of natural vegetation</td><td>Bally sterophyllous vegetation in a climax stage of development, including maquis, matellar and garigue.<br>Transitional bushy and herbaceous vegetation with occasional scattered trees. Can represent woodland degradation, forest regeneration / recolonization or natural succession.<br>Natural non-vegetated areas of sand or pebble gravel, in coastal or continental locations, like beaches, dunes, gravel pads, including beds of stream channels with torrential regime. Vegetation covers maximum 10%.</td></tr><tr><td>22</td><td>Agro-forestry areas</td><td>Scree, chills, rock outcrops, including areas of active erosion, rocks and reef flats situated above the high-water mark, inland salt planes.</td></tr><tr><td>23</td><td>Broad-leaved forest</td><td>Areas with sparse vegetation, covering 10-50% of surface. Includes steppes, tundra, Indian heath, badlands, karsitic areas and scattered high-altitude vegetation.</td></tr><tr><td>24</td><td>Coniferous forest</td><td>Natural woody vegetation affected by recent fires.</td></tr><tr><td>25</td><td>Mixed forest</td><td>Land covered by glaciers or permanent snowfields. Permanent snow and ice can be captured by finding the patches' smallest extent during the year. This can be captured when they shrink to minimum due to summer warmth, but before the first snowfall after summer occurs. Such ideal date is between end July and late September.</td></tr><tr><td>26</td><td>Natural grassland</td><td>Low-lying land usually flooded in winter, and with ground more or less saturated by fresh water all year round.<br>Wetlands with accumulation of considerable amount of decomposed moss (mostly Sphagnum) and vegetation matter. Both natural and exploited peat bogs.</td></tr><tr><td>27</td><td>Moor and heathland</td><td>Vegetated low-lying areas in the coastal zone, above the high-tide line, susceptible to flooding by seawater. Often in the process of living filled in by coastal mud and sand sediments, gradually being colonized by halophytic plants.</td></tr><tr><td>28</td><td>Sclerophyllous vegetation</td><td>Sal-pans for extraction of salt from salt water by evaporation, active or in process of abandonment. Sections of salt marsh exploited for the production of salt, clearly distinguishable from the rest of the marsh by their parellelation and embankment systems.</td></tr><tr><td>29</td><td>Transitional woodland/shrub</td><td>Coastal zone under tidal influence between open sea and land, which is flooded by sea water regularly twice a day in a 12 hours cycle. Area between the average lowest and highest sea water level at low tide and high tide. Generally non-vegetated areas of mud, sand or rock lying, between high and low water marks.</td></tr><tr><td>30</td><td>Beaches, dunes, and sand plains</td><td>Natural or artificial water-courses serving as water drainage channels. Includes canals. Minimum width for inclusion: 100 m.</td></tr><tr><td>31</td><td>Bare rock</td><td>Natural or artificial water bodies with presence of standing water surface during most of the year.</td></tr><tr><td>32</td><td>Sparingly vegetated areas</td><td>Stretches of salt or brackish water in coastal areas which are separated from the sea by a tongue of land or other similar topography. These water bodies can be connected to the sea at limited points, either permanently or for parts of the year only.</td></tr><tr><td>33</td><td>Burnt areas</td><td>The mouth of a river under tidal influence within which the tide ebbs and flows.</td></tr><tr><td>34</td><td>Glaciers and perpetual snow</td><td>Zone seaward of the lowest tide limit.</td></tr><tr><td>35</td><td>Inland marshes</td><td></td></tr><tr><td>36</td><td>Peatbogs</td><td></td></tr><tr><td>37</td><td>Coastal salt marshes</td><td></td></tr><tr><td>38</td><td>Salines</td><td></td></tr><tr><td>39</td><td>Intertidal flats.</td><td></td></tr><tr><td>40</td><td>Water courses</td><td></td></tr><tr><td>41</td><td>Water bodies.</td><td></td></tr><tr><td>42</td><td>Coastal lagoons</td><td></td></tr><tr><td>43</td><td>Estuaries</td><td></td></tr><tr><td>44</td><td>Sea and ocean</td><td></td></tr></tbody></table>
<table><caption>Table D.2: OSO Class definition</caption><thead><tr><th>label</th><th>title</th><th>text</th></tr></thead><tbody><tr><td>1</td><td>Continuous urban fabric</td><td>Most of the land is covered by structures and the transport network. Building, roads and artificially surfaced areas cover more than 80% of the total surface. Non-linear areas of vegetation and bare soil are exceptional.</td></tr><tr><td>2</td><td>Discontinuous urban fabric</td><td>Most of the land is covered by structures. Building, roads and artificially surfaced areas associated with vegetated areas and bare soil, which occupy discontinuous but significant surfaces.</td></tr><tr><td>3</td><td>Industrial or commercial units</td><td>Artificially surfaced areas (with concrete, asphalt, tarmacadam, or stabilised, e.g., beaten earth) without vegetation occupy most of the area, which also contain buildings and/or vegetation.</td></tr><tr><td>4</td><td>Road surfaces</td><td>Motorway rest areas, parking areas, motorway networks, larger than 50 m.</td></tr><tr><td>5</td><td>Rapeseed</td><td>Rapeseed usually seeded between November and February and harvested at the beginning of the summer (mid June to the end of July).</td></tr><tr><td>6</td><td>cereals</td><td>Straw cereal crops usually seeded between November and February and harvested at the beginning of the summer (mid June to the end of July).</td></tr><tr><td>7</td><td>protein crops</td><td>Protein crops usually seeded between November and February and harvested at the beginning of the summer (mid June to the end of July).</td></tr><tr><td>8</td><td>soy</td><td>Soy crops usually seeded from March to mid June and harvested at the end of the summer (mid August to mid September).</td></tr><tr><td>9</td><td>turnsole</td><td>Sunflower usually seeded from March to mid June and harvested at the end of the summer (mid August to mid September).</td></tr><tr><td>10</td><td>corn</td><td>Maize usually seeded from March to mid June and harvested at the end of the summer (mid August to mid September).</td></tr><tr><td>11</td><td>rice</td><td>Rice usually seeded from March to mid June and harvested at the end of the summer (mid August to mid September).</td></tr><tr><td>12</td><td>tubers</td><td>Tubers or roots usually seeded from March to mid June and harvested at the end of the summer (mid August to mid September).</td></tr><tr><td>13</td><td>Intensive grassland</td><td>Dense grass cover, of floral composition characterized by agricultural use or strong human disturbance not under a rotation system used.</td></tr><tr><td>14</td><td>Orchards</td><td>Agricultural lands parcels planted with fruit trees or shrubs.</td></tr><tr><td>15</td><td>Vineyards</td><td>Agricultural lands areas planted with vines.</td></tr><tr><td>16</td><td>Broad-leaved forest</td><td>Area populated by forest trees covering at least 40% of the soil, and composed of more than 75% of hardwoods (relative coverage rate). Young hardwood plantations, natural reforestation and clearcuts of these species are also entered.</td></tr><tr><td>17</td><td>Coniferous forest</td><td>Area populated by forest trees covering at least 40% of the soil and composed mainly of conifers (i.e. a relative cover rate of more than 75%). Young coniferous plantations, natural reforestation and clearcutting of these species are also entered.</td></tr><tr><td>18</td><td>Natural grassland</td><td>Grasslands under no or moderate human influence. Low productivity grasslands. Often situated in areas of rough, uneven ground, steep slopes, frequently including rocky areas, briars and heathland.</td></tr><tr><td>19</td><td>Woody moorlands</td><td>Spontaneous vegetation dominated by woody plants (heather, briar, broom, etc.) and semi-woody plants (fern, phragmites, etc.) shorter than 5 m.</td></tr><tr><td>20</td><td>Bare rock</td><td>Scree, cliffs, rock outcrops, including areas of active erosion, rocks and reef flats situated above the high-water mark.</td></tr><tr><td>21</td><td>Beaches, dunes, and sand plains</td><td>beaches, dunes and expanses of sand or pebbles in coastal or continental locations, including beds of stream channels with torrential regime.</td></tr><tr><td>22</td><td>Glaciers and perpetual snow</td><td>land covered by glaciers or permanent snowfields.</td></tr><tr><td>23</td><td>Water bodies</td><td>All water bodies longer than 20 m and all water courses larger than 7.5 m</td></tr></tbody></table>
<table><caption>Table D.3: MOS classes definitions</caption><thead><tr><th>label</th><th>title</th><th>text</th></tr></thead><tbody><tr><td>1</td><td>Forest</td><td>Vegetation of trees, shrubs, bushes that may result from regeneration or shrub recolonization. Area composed of at least 40% of trees 5m high (except orchards), including tree heaths. Glades, regeneration cuts, clear cuts, seedlings, including poplar forest cuts.</td></tr><tr><td>2</td><td>Semi-natural areas</td><td>Wetlands, marshes, non-treed moors, sparse or herbaceous vegetation, agricultural wastelands including fallows and multi-year frosts, abandoned quarries with vegetation, maneuvering grounds, deforestation rights-of-way for power lines or aqueducts and undeveloped banks of waterways.</td></tr><tr><td>3</td><td>Agricultural areas</td><td>Areas occupied by annual crops or grass areas except lawns mainly grazed but from which fodder can be harvested, including those of equestrian centers. Also includes Nurseries and fruit crops of more than 1000m² homogeneous or mixed and of commercial production, vine as well as abandoned or fallow orchards, vegetable crops (salads, etc.), market gardening, flower crops and greenhouse crops.</td></tr><tr><td>4</td><td>Water</td><td>Water surfaces of at least 500m², including park ponds, groundwater layers of gravel pits and retention basins. Permanent water courses without maximum width restriction, including canals. Parks. Pleasure gardens, vegetable gardens or orchards gardens, for family use. Outdoor courts. Outdoor bathing areas. Roller-skating and cross-country tracks. Open shooting ranges. Evolution parks for the practice of golf, including buildings. Equipment for horse racing. Campgrounds and caravans, including mobile homes. Zoos, amusement parks, recreation centers without accommodation. Non-agricultural grass surfaces: military maneuver grounds, near airfield runways, grassed areas in business and commercial areas, castles. Vacant or undeveloped land, located within the urban grid.</td></tr><tr><td>6</td><td>Individual housing</td><td>Housing estates and individual buildings. Groups of buildings spaced less than 100m apart, mostly rural in form from 1 to 2 levels, exceptionally 3, built contiguously forming a built core, comprising in its central part a point of convergence or a particular point (monument, church), including buildings farm, including a road structure whose narrow width and layout testify to a road of village origin. Castles will be classified in low continuous habitat for the building itself, and in village for the outbuildings.</td></tr><tr><td>7</td><td>Collective housing</td><td>They includes continuous and discontinuous collective or individual housings in cities. The areas concerned are in the old suburbs, centers and in the new "townhouse" districts. Includes castles, car parks, green spaces, shops, play areas that are an integral part of the whole, prisons, hotels (excluding activity zones), reception centers, holiday and leisure centers, homes for workers and students, convents, seminars, retirement homes, precarious or mobile housing (caravans or mobile isolated homes).</td></tr><tr><td>8</td><td>Activity zone</td><td>Industrial plants. Industrial activities (in business premises, laboratories, warehouses, workshops, etc.) dispersed in residential areas, thus forming a mixed fabric, but which are individualized in relation to the habitat. Animal production activities: kennels, stud farms, poultry facilities, veterinarians, etc. Parking lots and large vacant spaces. Storage areas for new vehicles, caravans, construction materials, sawmills, vehicle scrap yards, including port areas. Logistics warehouses. Commercial areas with surface greater than 400m². Gas stations. Offices of more than 5000m².</td></tr><tr><td>9</td><td>amenities</td><td>Indoor and outdoor sports facilities. Outdoor and indoor swimming pool including biological pools. Autodromes including a speed or road track. All schools public or private. Hospital. Large convention and exhibition centers. Museums, some libraries, castles open to the public. All administrations, police stations, fire stations. Large public installations including military, radio installations. Permanent markets. Places of worship. Nurseries, leisure activity buildings, post offices, motorway tolls and locks.</td></tr><tr><td>10</td><td>transport network</td><td>Yard yards, stations, equipment maintenance facilities, rail tracks including fill and cut. Tracks > 25m wide, including access ramps, embankment and cuttings embankments, entire interchanges. Car parks with their own right of way, excluding underground parks. This item includes car parks associated with equipment and housing. Bus, bus and coach stations for travelers. Freight transport facilities are identified in storage activities. This item includes RATP bus depots. Air terminals, aircraft parking areas, technical installations (hangars, etc.) and runways only are included in this station.</td></tr><tr><td>11</td><td>Quarries, landfills and construction sites</td><td>Quarries, sand pits in activity or abandoned, without traces of vegetation. Authorized landfills, waste reception centers and landfill areas. Construction and demolition sites.</td></tr></tbody></table>
<table><caption>Table D.4: OCS-GEc classes definitions</caption><thead><tr><th>label</th><th>title</th><th>text</th></tr></thead><tbody><tr><td>1</td><td>Built-up areas</td><td>Areas covered with buildings or other types of construction. These areas include buildings of a permanent nature, covered with a roof and the associated spaces in compliance with the entry thresholds. They are intended to shelter, house or place people, animals, material, goods. Includes glass greenhouses or plastic tunnels capable of accommodating a standing man.</td></tr><tr><td>2</td><td>Undeveloped areas</td><td>Areas covered with patrially or fully waterproofed materials, in particular asphalt, concrete, paved or slab floors. The paved road network, squares, car parks and tennis courts regardless of the surface are impermeable non-built areas.</td></tr><tr><td>3</td><td>Mineral material areas</td><td>Areas covered with stabilized and compacted soils, partially or totally permeable, and covered with mineral materials. Includes railroad networks, stone paths, forest tracks or services, non-vegetated firewalls, transport track sites (roads, motorways, railways, etc.) quarries, salt works and construction sites (groynes and rip-rap).</td></tr><tr><td>4</td><td>Areas with other composite materials</td><td>Areas covered with heterogeneous and artificial materials with a mixture of non-mineral materials. Includes, in particular, dumpsites.</td></tr><tr><td>5</td><td>Bare soils</td><td>Areas covered with natural bare soil. Includes soils covered with sand, pebbles, rocks, stony surfaces or any other mineral material.</td></tr><tr><td>6</td><td>Water surfaces</td><td>Areas permanently covered with water. Includes submerged surfaces (Areas permanently covered with water). The limits are the banks or vegetation without the rate of coverage exceeding 25%).</td></tr><tr><td>7</td><td>Snowfields and glaciers</td><td>Areas permanently covered with snow or ice. Includes surfaces covered mainly by glaciers and snowfields.</td></tr><tr><td>8</td><td>Broad-leaved trees</td><td>Areas only covered with deciduous trees. This class includes pure stands of the same species of hardwoods or a mixture of hardwoods (oak, beech, poplars, fruit trees...). This class excludes woodland pastures. Absolute tree cover rate greater than or equal to 25% with a free coverage rate relative hardwood greater than or equal to 75%.</td></tr><tr><td>9</td><td>Coniferous trees</td><td>Areas only covered with coniferous trees. This class includes pure stands of the same species of conifers or a mixture of conifers (pines, firs...). Absolute tree cover rate greater than or equal to 25% with a free coverage rate relative hardwood greater than or equal to 75%.</td></tr><tr><td>10</td><td>Mixed trees</td><td>Areas covered with a mixed of deciduous and coniferous trees. This class includes mixed stands of hardwoods and conifers (oak-beech forest, beech forest pinewood, etc.) in massifs or linearly (wooded cordons). Absolute tree cover rate greater than or equal to 25% with a free coverage rate relative hardwood greater than or equal to 75%.</td></tr><tr><td>11</td><td>Shrub and sub-shrub formations</td><td>Areas covered with shrubs and sub-shrubs. This class includes alpine moors, moors mountain areas, non-wooded scrubland or maquis, uncultivated or fallow land, the moors on salty land, phragmites heaths. This class also includes plantations of small fruits, horticultural plants, medicinal plants and Aromatic...</td></tr><tr><td>12</td><td>Liana-like vegetation</td><td>Areas covered with liana-like vegetation. Includes plantations of vines, hops, kiwi...</td></tr><tr><td>13</td><td>Herbaceous formations</td><td>Areas covered with herbaceous vegetation. This class includes permanent and temporary meadows, natural lawns, arable land, ornamental lawns, collective or individual market gardens in the vicinity of dwellings, vegetable lawns in sports complexes, etc.</td></tr><tr><td>14</td><td>Other non-woody formations</td><td>Areas covered with other non-woody formations. Includes Areas covered with lichen or moss. Includes areas of banana, palm or bamboo.</td></tr></tbody></table>
<table><caption>Table D.5: OCS-GEU classes definition</caption><thead><tr><th>label</th><th>title</th><th>text</th></tr></thead><tbody><tr><td>1</td><td>Agriculture</td><td>Production of crops (plants, mushrooms, etc.) and animal products for food, sale, home consumption or industrial uses. This category includes crops intended for the production of biofuels as well as field cultivation and cultivation under cover. Land fallow under rotation also falls into this category. The preparation of products for their primary marketing is included, as well as land reclamation (earthworks, drainage, preparation of rice fields, etc.) as well as landscaping and maintenance. This class includes agricultural infrastructure.</td></tr><tr><td>2</td><td>Forestry</td><td>Production of roundwood and other primary wood products. In addition to wood production, forestry activities generate products that undergo limited processing, such as firewood, charcoal, and roundwood used in unprocessed form (e.g., minewood, pulpwood, etc.). Nurseries as well as storage and transport areas related to the exploitation of timber, trees and woody plants intended for the production of biofuels are also covered. These activities can be carried out in natural forests or in plantations. Extractive industries consisting of the extraction of minerals and materials occurring naturally in the form of solids (coal, minerals, gravel, sand, salt), liquids (petroleum), gas (natural gas) or biomass (peat). Extraction can be done in different ways: underground, on the surface, by digging wells, etc. This class includes buildings and infrastructure related to these activities.</td></tr><tr><td>3</td><td>Extraction activities</td><td>This category includes professional fishing and inland or marine aquaculture (fish farming, seaweed farming, shellfish farming, mussel farming, carcinoculture, echinoculture). This class includes buildings and infrastructure related to these activities.</td></tr><tr><td>4</td><td>Fisheries and Aquaculture</td><td>Industrial and manufacturing activities consisting in manufacturing, from the output of the primary sector, manufactured goods and intermediate products for other sectors. Services constituting products for other businesses and consumers, both private and public. This category includes commercial services, financial services, specialist services and information services, public services, cultural services, leisure services and recreational services. Areas mainly used for housing people.</td></tr><tr><td>5</td><td>Commercial, industrial and residential areas</td><td>Areas used for road transport as well as associated infrastructure, for example, roads, car parks or service stations, rest area ...</td></tr><tr><td>6</td><td>Road networks</td><td>Areas used for rail transport as well as associated infrastructure, for example, tracks, railway stations and marshalling yards, parking ...</td></tr><tr><td>7</td><td>Rail networks</td><td>Areas used for air transport as well as associated infrastructure, e.g. airports and related services, ...</td></tr><tr><td>8</td><td>Air transport network</td><td>Areas used for inland waterway transport and associated infrastructure, for example ports, rivers, docks and related services, ... This class includes canals dedicated to navigation.</td></tr><tr><td>9</td><td>River and maritime transport networks</td><td>Areas used for separate storage and logistics services (not directly related to industries)</td></tr><tr><td>10</td><td>Logistics and storage services</td><td>Infrastructures linked to public utility networks grouping the areas used for the distribution of electricity, gas and thermal energy, the areas used for the collection, collection, purification, storage and distribution of water as well as only for the collection and treatment of wastewater, areas used for the collection and recycling of waste. This class includes areas regularly maintained (shredding) under high-voltage lines (interruption across the width of the tree cover).</td></tr><tr><td>11</td><td>Public utility networks</td><td>Areas under construction.</td></tr><tr><td>12</td><td>Transitional areas</td><td>Agricultural, residential or industrial areas and areas devoted to transport and basic infrastructure in a derelict state. An area belongs to the category of abandoned areas if it is no longer in use or can no longer be used for its original purposes without major repair or renovation work.</td></tr><tr><td>13</td><td>Abandoned areas</td><td>Areas which occur in their natural state and are not subject to other economic use.</td></tr><tr><td>14</td><td>Unused</td><td>Areas which occur in their natural state and are not subject to other economic use.</td></tr></tbody></table>
<table><caption>Table D.6: CGLS classes definition</caption><thead><tr><th>label</th><th>title</th><th>text</th></tr></thead><tbody><tr><td>1</td><td>Closed forest.</td><td>Forest with tree canopy covering more than 70% of the area.</td></tr><tr><td>2</td><td>Open forest</td><td>Forest with top layer-trees covering 15% to 70% of the area and second layer-mixed of shrubs and grassland.<br>These are woody perennial plants with persistent and woody stems and without any defined main stem being less than 5 m tall. The shrub foliage can be either evergreen or deciduous.</td></tr><tr><td>3</td><td>Shrubs</td><td>Plants without persistent stem or shoots above ground and lacking definite firm structure. Tree and shrub cover is less than 10 %.</td></tr><tr><td>4</td><td>Herbaceous vegetation</td><td>Lands with a permanent mixture of water and herbaceous or woody vegetation. The vegetation can be present in either salt, brackish, or fresh water.</td></tr><tr><td>5</td><td>Herbaceous wetland</td><td>Moss and lichen</td></tr><tr><td>6</td><td>Moss and lichen</td><td>Lands with exposed soil, sand, or rocks and never has more than 10 % vegetated cover during any time of the year</td></tr><tr><td>7</td><td>Bare / sparse vegetation</td><td>Lands covered with temporary crops followed by harvest and a bare soil period (e.g., single and multiple cropping systems). Note that perennial woody crops will be classified as the appropriate forest or shrub land cover type.</td></tr><tr><td>8</td><td>Cultivated and managed vegetation/agriculture (cropland)</td><td>Land covered by buildings and other man-made artificial structures</td></tr><tr><td>9</td><td>Urban / built up</td><td>Lands under snow or ice cover throughout the year.</td></tr><tr><td>10</td><td>Snow and Ice</td><td>lakes, reservoirs, and rivers. Can be either fresh or salt-water bodies.</td></tr><tr><td>11</td><td>Permanent water bodies</td><td>Oceans, seas. Can be either fresh or salt-water bodies.</td></tr><tr><td>12</td><td>Open sea</td><td></td></tr></tbody></table>
<table><caption>Table D.7: ESRI land-cover map <sup>a</sup></caption><thead><tr><th>label</th><th>title</th><th>text</th></tr></thead><tbody><tr><td>1</td><td>Water</td><td>Areas where water was predominantly present throughout the year. may not cover areas with sporadic or ephemeral water. contains little to no sparse vegetation, no rock outcrop nor built up features like docks. examples: rivers, ponds, lakes, oceans, flooded salt plains.</td></tr><tr><td>2</td><td>Trees</td><td>Any significant clustering of tall (15-m or higher) dense vegetation, typically with a closed or dense canopy. examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).</td></tr><tr><td>3</td><td>Flooded vegetation</td><td>Areas of any type of vegetation with obvious intermixing of water throughout a majority of the year. seasonally flooded area that is a mix of grass/shrub/trees/bare ground. examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.</td></tr><tr><td>4</td><td>Crops</td><td>Human planted/plotted cereals, grasses, and crops not at tree height. examples: corn, wheat, soy, fallow plots of structured land.</td></tr><tr><td>5</td><td>Built Area</td><td>Human made structures. major road and rail networks. large homogenous impervious surfaces including parking structures, office buildings and residential housing. examples: houses, dense villages / towns / cities, paved roads, asphalt.</td></tr><tr><td>6</td><td>Bare ground</td><td>Areas of rock or soil with very sparse to no vegetation for the entire year. large areas of sand and deserts with no to little vegetation. examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.</td></tr><tr><td>7</td><td>Snow/Ice</td><td>Large homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes. examples: glaciers, permanent snowpack, snow fields.</td></tr><tr><td>8</td><td>Clouds</td><td>No land cover information due to persistent cloud cover.</td></tr><tr><td>9</td><td>Rangeland</td><td>Open areas covered in homogenous grasses with little to no taller vegetation. wild cereals and grasses with no obvious human plotting (i.e., not a plotted field). examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock. scrub-filled clearings within dense forests that are clearly not taller than trees. examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.</td></tr></tbody></table>

<footnote><sup>a</sup>https://www.arcgis.com/home/item.html?id=d6642f8a4f6d4685a24ae2dc0c73d4ac</footnote>
<table><caption>Table D.8: MCD12Q1: IGBP nomenclature</caption><thead><tr><th>label</th><th>title</th><th>text</th></tr></thead><tbody><tr><td>1</td><td>Evergreen Needleleaf Forests</td><td>Dominated by evergreen conifer trees (canopy &gt;2m). Tree cover &gt;60%.</td></tr><tr><td>2</td><td>Evergreen Broadleaf Forests</td><td>Dominated by evergreen broadleaf and palmate trees (canopy &gt;2m). Tree cover &gt;60%.</td></tr><tr><td>3</td><td>Deciduous Needleleaf Forests</td><td>Dominated by deciduous needleleaf (larch) trees (canopy &gt;2m). Tree cover &gt;60%.</td></tr><tr><td>4</td><td>Deciduous Broadleaf Forests</td><td>Dominated by deciduous broadleaf trees (canopy &gt;2m). Tree cover &gt;60%.</td></tr><tr><td>5</td><td>Mixed Forests</td><td>Dominated by neither deciduous nor evergreen (40-60% of each) tree type (canopy &gt;2m). Tree cover &gt;60%.</td></tr><tr><td>6</td><td>Closed Shrublands</td><td>Dominated by woody perennials (1-2m height) &gt;60% cover.</td></tr><tr><td>7</td><td>Open Shrublands</td><td>Dominated by woody perennials (1-2m height) 10-60% cover.</td></tr><tr><td>8</td><td>Woody Savannas</td><td>Tree cover 30-60% (canopy &gt;2m).</td></tr><tr><td>9</td><td>Savannas</td><td>Tree cover 10-30% (canopy &gt;2m).</td></tr><tr><td>10</td><td>Grasslands</td><td>Dominated by herbaceous annuals (&lt;2m).</td></tr><tr><td>11</td><td>Permanent Wetlands</td><td>Permanently inundated lands with 30-60% water cover and &gt;10% vegetated cover.</td></tr><tr><td>12</td><td>Croplands</td><td>At least 60% of area is cultivated cropland.</td></tr><tr><td>13</td><td>Urban and Built-up Lands</td><td>At least 30% impervious surface area including building materials, asphalt, and vehicles.</td></tr><tr><td>14</td><td>Cropland/Natural Vegetation Mosaics</td><td>Mosaics of small-scale cultivation 40-60% with natural tree, shrub, or herbaceous vegetation.</td></tr><tr><td>15</td><td>Permanent Snow and Ice</td><td>At least 60% of area is covered by snow and ice for at least 10 months of the year.</td></tr><tr><td>16</td><td>Barren</td><td>At least 60% of area is non-vegetated barren (sand, rock, soil) areas with less than 10% vegetation.</td></tr><tr><td>17</td><td>Water Bodies</td><td>At least 60% of area is covered by permanent water bodies.</td></tr></tbody></table>
<table><caption>Table D.9: MCD12Q1: UMD nomenclature</caption><thead><tr><th>label</th><th>title</th><th>text</th></tr></thead><tbody><tr><td>0</td><td>Water bodies</td><td>At least 60% of area is covered by permanent water bodies.</td></tr><tr><td>1</td><td>Evergreen Needleleaf Forests</td><td>Dominated by evergreen conifer trees (canopy &gt;2m). Tree cover &gt;60%.</td></tr><tr><td>2</td><td>Evergreen Broadleaf Forests</td><td>Dominated by evergreen broadleaf and palmate trees (canopy &gt;2m). Tree cover &gt;60%.</td></tr><tr><td>3</td><td>Deciduous Needleleaf Forests</td><td>Dominated by deciduous needleleaf (larch) trees (canopy &gt;2m). Tree cover &gt;60%.</td></tr><tr><td>4</td><td>Deciduous Broadleaf Forests</td><td>Dominated by deciduous broadleaf trees (canopy &gt;2m). Tree cover &gt;60%.</td></tr><tr><td>5</td><td>Mixed Forests</td><td>Dominated by neither deciduous nor evergreen (40-60% of each) tree type (canopy &gt;2m). Tree cover &gt;60%.</td></tr><tr><td>6</td><td>Closed Shrublands</td><td>Dominated by woody perennials (1-2m height) &gt;60% cover.</td></tr><tr><td>7</td><td>Open Shrublands</td><td>Dominated by woody perennials (1-2m height) 10-60% cover</td></tr><tr><td>8</td><td>Woody Savannas</td><td>Tree cover 30-60% (canopy &gt;2m).</td></tr><tr><td>9</td><td>Savannas</td><td>Tree cover 10-30% (canopy &gt;2m).</td></tr><tr><td>10</td><td>Grasslands</td><td>Dominated by herbaceous annuals (&lt;2m).</td></tr><tr><td>11</td><td>Permanent Wetlands</td><td>Permanently inundated lands with 30-60% water cover and &gt;10% vegetated cover.</td></tr><tr><td>12</td><td>Croplands</td><td>At least 60% of area is cultivated cropland.</td></tr><tr><td>13</td><td>Urban and Built-up Lands</td><td>At least 30% impervious surface area including building materials, asphalt, and vehicles.</td></tr><tr><td>14</td><td>Cropland/Natural Vegetation Mosaics</td><td>Mosaics of small-scale cultivation 40-60% with natural tree, shrub, or herbaceous vegetation.</td></tr><tr><td>15</td><td>Non-Vegetated Lands</td><td>At least 60% of area is non-vegetated barren (sand, rock, soil) or permanent snow and ice with less than 10% vegetation.</td></tr></tbody></table>
<table><caption>Table D.10: MCD12Q1: LAI nomenclature</caption><thead><tr><th>label</th><th>title</th><th>text</th></tr></thead><tbody><tr><td>0</td><td>Water bodies</td><td>At least 60% of area is covered by permanent water bodies.</td></tr><tr><td>1</td><td>Grasslands</td><td>Dominated by herbaceous annuals (&lt;2m) including cereal croplands.</td></tr><tr><td>2</td><td>Shrublands</td><td>Shrub (1-2m) cover &gt;10%.</td></tr><tr><td>3</td><td>Broadleaf Croplands</td><td>Dominated by herbaceous annuals (&lt;2m) that are cultivated with broadleaf crops.</td></tr><tr><td>4</td><td>Savannas</td><td>Between 10-60% tree cover (&gt;2m).</td></tr><tr><td>5</td><td>Evergreen Broadleaf Forests</td><td>Dominated by evergreen broadleaf and palmate trees (canopy &gt;2m). Tree cover &gt;60%.</td></tr><tr><td>6</td><td>Deciduous Broadleaf Forests</td><td>Dominated by deciduous broadleaf trees (canopy &gt;2m). Tree cover &gt;60%.</td></tr><tr><td>7</td><td>Evergreen Needleleaf Forests</td><td>Dominated by evergreen conifer trees (canopy &gt;2m). Tree cover &gt;60%.</td></tr><tr><td>8</td><td>Deciduous Needleleaf Forests</td><td>Dominated by deciduous needleleaf (larch) trees (canopy &gt;2m). Tree cover &gt;60%.</td></tr><tr><td>9</td><td>Non-Vegetated Lands</td><td>At least 60% of area is non-vegetated barren (sand, rock, soil) or permanent snow and ice with less than 10% vegetation.</td></tr><tr><td>10</td><td>Urban and Built-up Lands</td><td>At least 30% impervious surface area including building materials, asphalt, and vehicles.</td></tr></tbody></table>
Confusion matrix of CGLS-LC100, CLC, OSO
on our ground truth

<figure><img src="image_105.png" /><figcaption>Figure E.1: Confusion matrix of CGLS-LC100 computed on our ground truth normalized by number of elements predicted, i.e. Color on the diagonal reflects per-class precision.</figcaption></figure>
<figure><img src="image_106.png" /><figcaption>Figure E.2: Confusion matrix of CGLS-LC100 computed on our ground truth normalized by number of elements in the ground truth, i.e. Color on the diagonal reflects per-class recall.</figcaption></figure>
<figure><img src="image_107.png" /><figcaption>Figure E.3: Confusion matrix of CLC computed on our ground truth normalized by number of elements predicted, i.e. Color on the diagonal reflects per-class precision.</figcaption></figure>
<figure><img src="image_108.png" /><figcaption>Figure E.4: Confusion matrix of CLC computed on our ground truth normalized by number of elements in the ground truth, i.e. Color on the diagonal reflects per-class recall.</figcaption></figure>
<figure><img src="image_109.png" /><figcaption>Figure E.5: Confusion matrix of OSO computed on our ground truth normalized by number of elements predicted, i.e. Color on the diagonal reflects per-class precision.</figcaption></figure>
<figure><img src="image_110.png" /><figcaption>Figure E.6: Confusion matrix of OSO computed on our ground truth normalized by number of elements in the ground truth, i.e. Color on the diagonal reflects per-class recall.</figcaption></figure>
# Assessing sampling error uncertainty

As we did not find published work on estimating per-class Fscore margin of error depending on the ground truth size, this appendix provide a simple statistical formula. We use the notation introduce in Figure F.1 in our formulas.

<table><caption>Table F.1: Confusion Matrix illustration</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Total population<br>= P + N</th><th colspan="2">Predicted class</th></tr><tr><th>Positive (PP)</th><th>Negative (PN)</th></tr></thead><tbody><tr><th rowspan="2">Ground truth class</th><td>Positive (P)</td><td>True positive (TP)</td><td>False negative (FN)</td></tr><tr><td>Negative (N)</td><td>False positive (FP)</td><td>True negative (TN)</td></tr></tbody></table>

We define the following values:

$$TPR = recall = \frac{TP}{P} = \frac{TP}{TP + FN}. \qquad (F.1)$$

$$TNR = \frac{TN}{N} = \frac{TN}{TN + FP}. \qquad (F.2)$$

$$PPV = precision = \frac{TP}{TP + FP}. \qquad (F.3)$$

$$F1_{score} = 2 \frac{precision \times recall}{precision + recall} = 2 \frac{PPV \times TPR}{PPV + TPR} = 2 \frac{A}{B} \qquad (F.4)$$

Margin for *TPR* and *TNR* values can be computed [52, 230]. Assuming a confidence level of 95% ($z = 1.96$) and *n* the per class sample size, *TPR* (same for *TNR*) is computed using Equation F.5 :

$$\delta_{TPR} = z \sqrt{\frac{TPR(1 - TPR)}{n}}. \qquad (F.5)$$

To estimate *PPV* uncertainty we rewrite Equation F.3 using F.1 and F.2. We obtain Equation F.6 :

$$PPV = \frac{TP}{TP + FP} = \frac{P \times TPR}{P \times TPR + N(1 - TNR)} = \frac{C}{D}. \qquad (F.6)$$
As *P* and *N* corresponds to ground truth values, they are considered exact (with no uncertainties). Then we can compute uncertainties for PPV by merging Equation F.6 and F.5. We obtain Equation F.9 :

$$ \delta_C = P\delta_{TPR}. \qquad (F.7) $$

$$ \delta_D = P\delta_{TPR} + N\delta_{TNR}. \qquad (F.8) $$

$$ \delta_{PPV} = PPV\left(\frac{\delta_C}{C} + \frac{\delta_D}{D}\right). \qquad (F.9) $$

Finally, by merging Equation F.4 with F.5 and F.9 we obtain Equation F.14 :

$$ A = PPV \times TPR. \qquad (F.10) $$

$$ B = PPV + TPR. \qquad (F.11) $$

$$ \delta_A = A\left(\frac{\delta_{PPV}}{PPV} + \frac{\delta_{TPR}}{TPR}\right). \qquad (F.12) $$

$$ \delta_B = \delta_{PPV} + \delta_{TPR}. \qquad (F.13) $$

$$ \delta_{F1} = 2\frac{A}{B}\left(\frac{\delta_A}{A} + \frac{\delta_B}{B}\right). \qquad (F.14) $$
# Machine Learning and Deep Learning: short introduction

This section concisely introduces the main principle behind machine learning and con-
volution neural networks to facilitate reading for users with limited knowledge of those
techniques. For an in-depth review of all the underlying concepts, refer to [27, 101, 155].

## G.1 Machine Learning

Machine learning englobes a set of methods (including deep learning) designed to solve a problem by implicitly modelling a set of discriminative rules using real data. An illustration of the difference between solving a problem with machine learning and imperative approaches can be given using the example of water areas detection on optical satellite images. An imperative approach would consist in assuming a prior knowledge of water characteristics (e.g. water is blue) and writing an algorithm that will annotate all blue areas as water areas. Conversely, in a machine learning-based approach, one provides the algorithm with a set of areas annotated as water/not water and lets it determine a distinctive criterium to distinguish the water areas.

The phase allowing the method to produce the model to respond to the problem from the data provided is referred to as the learning or training phase. It is systematically followed by a validation phase carried out on another set of data to ensure the validity of the model obtained and its ability to generalize (i.e. its transferability to other data). If the network performs better on seen during training data than on unseen data (validation), the network is said to overfit.

Machine learning methods differ from each other both by the algorithms used for building the model and their training methods, that is to say, by the method making it possible to obtain the model. Thus, we distinguish supervised training, for which the data is labelled (we compare the expected output and the output for each input) from unsupervised training. In our land-cover scenario, we will always assume that we have an existing sample
of the desired target. Only supervised training methods are explored in our manuscript.

Deep Learning is a subset of the Machine learning algorithms for which the model is built using neural networks algorithm.

## G.2 Neural network

One of the multiple representations of the basic brick of a neural network is the perceptron [258].

<figure><img src="image_111.png" /><figcaption>Figure G.1: Perceptron[258]</figcaption></figure>

The following explanations are based on the figure G.1. Let ($X_1, X_2, ..., X_n$) be a set of input variables corresponding to data provided to the network. The neuron $\Sigma$ performs a weighted summation of these different input variables with per input variables weight denoted ($\omega_1, \omega_2, ..., \omega_n$). Finally, it applies an activation function denoted $a$ to this sum and returns the result. The activation function allows, among other things, the resolution of non-linear problems. A classic example of $a$ is the Rectified Linear Unit (ReLU) function defined as follows:

$$ReLU(x) = \begin{cases} 0 & \text{if } x < 0. \\ x & \text{if } x > 0. \end{cases} \qquad (G.1)$$

As explained above, machine learning methods are based on two phases (training and inference). In the case of neural networks, the training determines the ideal values of the $\omega_i$ to answer the stated problem. Training a neural network is an iterative process in which $\omega_i$ are initialised with random values that are progressively adjusted as training progresses. At each iteration, the network process a set of values ($X_1, X_2, ..., X_n$) input for which the expected output $y$ is known. A comparison between the output of the neurone and the expected value $y$ is then computed using a loss function. Multiple error functions have
been defined depending on the problem type (classification, regression) and the data to be processed. The choice of the loss function greatly conditions the result obtained.

Once the difference between obtained and expected results has been measured, we determine how to update the weights $\omega_i$ using the gradient descent algorithm. We set, $x^{(j)} = (x_1^{(j)}, x_2^{(j)}, ..., x_n^{(j)})$ the jth set of variables corresponding to a statistical individual and $x_i^{(j)}$ the ith variable of the jth set. We then call $f(x^{(j)})$ the output obtained by the perceptron for the jth set and $y^{(j)}$ the expected real value for this set. We can then write:

$$ \omega_i \leftarrow \omega_i - lr \frac{\partial \text{Loss}(f(x^{(j)}), y^{(j)})}{\partial \omega_i}. \qquad (G.2) $$

$lr$ is called the learning rate and is a network hyperparameter. If this parameter is too high, the network weights vary significantly at each iteration to provide a convergence towards a minimum for the considered $x^{(j)}$ and partly forget the learning carried out during the previous iterations. Conversely, if it is too weak, the network will need many iterations before obtaining a relevant result. If the error function used is the function Mean square error ou Ecart Moyen Quadratique (MSE), we can write based on the previous equation:

$$ \omega_i \leftarrow \omega_i - lr(y^{(j)} - f(x^{(j)}))x_i^{(j)}. \qquad (G.3) $$

We then iterate these operations on the $x^{(j)}$ at least until the error function converges. The resulting weight is then fixed, forming the model used for inference and marking the end of the training phase.

Deep learning or deep learning is based on stacking these building blocks in layers, the output of one of these layers constituting the input of the next (see figure G.2). We then update the $w_i$ (one per arrow on our diagram) by calculating the error on the last layer and back-propagating it. A challenge is then to ensure the convergence of the error function, which does not necessarily converge to the solution, but potentially to one of the many local minima. The choice of specific parameters (learning rate, number of layers, etc.) dramatically conditions the convergence capacity of the network. One of the many possible techniques to facilitate and accelerate the convergence of the network is to randomly group the observations into packets before submitting them to the network to limit the risk that the network over-learns at each iteration. These packets are called "batches".

Through this short introduction to the principles of neural networks, we identify two main points of interest that have been addressed during this internship:

* Deep Learning consists of a set of methods based on neural networks performing learning that can be supervised or unsupervised. The supervised or unsupervised approach often depends on the availability of ground truth. If possible, these two forms of learning should be studied in this study.

* Many hyper-parameters (parameters often chosen empirically) are involved in such networks: learning speed, number of layers, number of neurons in each layer, cost
function... Their choices significantly affect the results provided by the network. It
is therefore essential to study their influence.

<figure><img src="image_112.png" /><figcaption>Figure G.2: 3 layers Multi-Layer Perceptron ou Perceptron multi-couches (MLP)</figcaption></figure>

G.3 Convolution neural networks

As we seek to perform land-cover translation on rasterized land-cover, it is necessary to rely on deep learning architectures efficient in image processing. Such architectures mainly rely on the spatial filter convolution of an image. This type of network is arranged in the same way as on the MLP (see G.2). The input $X_i$ is then replaced by a matrix (an image) and the $\omega_i$ by convolution filters. The parameters of each filter are learned during the training phase and correspond to feature detectors (elements of interest) present in the image. The idea is that the network learns to recognize through these filters the important elements of an image to perform the expected task. The result of the convolution of an image by one of these filters is called an activation map (or feature map) and corresponds to the highlighting of the places where the feature is present. An example of convolution is presented in figure G.3.

<figure><img src="image_113.png" /><figcaption>Figure G.3: Convolution by a 3×3 filters</figcaption></figure>
A brief illustration could be given by taking the example of a simple 3-layer network of a neuron aiming to determine "green urban areas" on an image. A conceptual view of what could be learned by the network is that the first layer carries out a convolution by a filter aiming to detect grasslands. The second then seeks to determine on the activation map obtained if an element resembling a city is convolved by a grassland detector (since the city present in the activation map has also been convolved by the grassland filter). We then obtain a second activation map corresponding to the places where a green urban area is possible in a city. Finally, the last neuron sums the elements present on the feature map and returns 0 or 1 depending on the absence or presence of grassland in a city. This example illustrates, in particular, that the more the number of layers increases, the more the concepts learned become abstract, making it resolve the complex task.

Each convolution layer is characterized by its number of filters and the stride at which it
moves on the image. Filters are characterized by their size. For each convolution layer,
the number of parameters to be learned by the network is equal to the product between
the size of the filter and the number of filters present in the layer. We can classically break
down a CNN into four structuring elements that are successively stacked (see Figure G.4):

* A convolution layer: convolution between the features and the image to see if (and where) the feature is present. This is always at least the first layer of the network. Features are learned gradually during training. The number of feature maps, as well as the size of the features, are part of the hyperparameters of the network.

* A pooling layer (typically ReLU): we reduce the size of the feature maps. This considerably speeds up the calculation times (by reducing the number of pixels).

* A layer of normalisation (typically Batch Normalization (BN)): we normalise the input values to speed up learning and reduce the risk of over-fitting.

* An activation layer (typically ReLU).

Those four structuring elements are stacked multiple times to increase the depth of the
network into what is referred to as a layer. For instance, a two-layer network can be
composed of the first layer with a convolution followed by an activation layer and a second
layer, taking the output of the first layer and convolution and an activation layer as
input. An important observation is that at each convolution, the value of one pixel is
influenced by its neighbourhood. The convolution by a 3×3 filter modifies the value of
one pixel using the values of its eight neighbouring pixels. Stacked convolution increases
the size of the neighbourhood at each layer. The maximum neighbourhood size is termed
receptive field and is a key element in leveraging spatial context. A small receptive field
only enables to analyse of far-range influence. Various solutions exist to increase the
receptive field. The first strategy is to increase the network depth, as each convolution
will increase the receptive field. However, the bigger the network, the more complex the
training is due to vanishing gradients [176] and potential overfitting due to an increased
number of learnt parameters. The second strategy is to modify the convolution parameters by either using bigger filters (which is memory-consuming as it increases the number of learnt parameters) or dilated filters (instead of processing the nearest neighbours, the filter process the neighbours at a given distance). The last one is to modify the feature maps by reducing their size through, for example, a pooling layer.

Figure ?? shows the effect of different layers on the histogram of a feature map.

<figure><img src="image_114.png" /><figcaption>Figure G.4: Convolutional Neural network with a single layers</figcaption></figure>
EPI interpretation key

The Edge Preservation Index (see Equation 3.4) is highly correlated to the edge proportion in the reference. To distinguish 6 levels of EPI value (poor, slight, fair, moderate, substantial, almost perfect) we built an interpretation key based on a comparison with other traditional metrics previously (precision, recall, fscore, and Kappa). Figure H.1 presents the EPI, fscore and kappa value for different precision, recall values. As EPI and kappa value depends on the reference's edge proportion, those plots varies depending on the considered land-cover map. We display here the plot for the maps with the smaller (OCSGE use) and bigger (CGLS) edge proportion. The principal take away are listed below:

* Kappa and EPI value exhibits 0 value when the precision on edge prediction is identical to the edge proportion in the dataset. A value below 0 indicates that a random classifier replicating edge proportion would perform better than the evaluated classifier.

* When the reference's proportion of edges is high (right figure), the fscore is a bad indicator when its value is below 0.4 as its only takes into account variation in recall and is relatively insensible to broad precision variation.

* When the reference's proportion of edges is low (left figure), the fscore and the Kappa are bad indicators when their value are below 0.4 as they exhibits both a precision and recall asymptotic behavior when the precision and recall widely differ, i.e. they become insensible to wide precision or recall variation, only focusing on the lowest of the two values.

* Kappa, EPI and fscore exhibits almost the same pattern twoard precision and recall when they reach a 0.8 value making them all usable.

To sum up, the main advantage of the EPI compared to fscore and kappa is that it enable studying quality variation when the precision and recall even when the edges are poorly detected. Based on those plots we identify the 6 levels (poor, slight, fair, moderate, substantial, almost perfect) by assuming that each levels must represents the same overall
<figure><img src="image_115.png" /><figcaption>Figure H.1: EPI (black line and colormap), fscore (red lines) and kappa (white line), for different precision recall value. The left plot is computed assuming 13% edges (OCSGE use) while the right is computed assuming 40% edges (CGLS). We underline that since we assume the target proportion of edges in a binary classification setup (edge/non edge) some recall/precision couple can not be observed, in which case they are represented in white.</figcaption></figure>

<table><caption>Table H.1: EPI interpretation key, based on percentile of EPI values above 0 computed on diagrams such as those presented in Figure H.1. Value above the substantial threshold are termed almost perfect</caption><thead><tr><th>Target</th><th>CGLS</th><th>CLC</th><th>OSO</th><th>MOS</th><th>OCSGE cover</th><th>OCSGE use</th><th>Average</th></tr></thead><tbody><tr><td>Edge proportion</td><td>40</td><td>33</td><td>23</td><td>19</td><td>15</td><td>12</td><td></td></tr><tr><td>Poor</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Slight (percentile 20)</td><td>13</td><td>14</td><td>16</td><td>17</td><td>18</td><td>18</td><td>16</td></tr><tr><td>Fair (percentile 40)</td><td>26</td><td>28</td><td>30</td><td>31</td><td>32</td><td>32</td><td>30</td></tr><tr><td>moderate (percentile 60%)</td><td>40</td><td>42</td><td>45</td><td>46</td><td>47</td><td>47</td><td>45</td></tr><tr><td>Substantial (percentile 80%)</td><td>58</td><td>60</td><td>62</td><td>63</td><td>63</td><td>63</td><td>62</td></tr></tbody></table>

area on the graph except for the poor value that represent EPI value below 0. As those values depends on the proportion of edges we review the per land-cover results in Table H.1.
Map and S1/DEM fusion results

<table><caption>Table I.1: Comparison of various Fusion strategies using Sentinel-1, our 6 land cover maps and the MLCT-Net in terms of OA<sub>ag</sub>.</caption><thead><tr><th rowspan="2">Source</th><th rowspan="2">Target</th><th colspan="4">CGLS (P)</th><th colspan="4">CLC (C)</th><th colspan="4">OSO (O)</th><th colspan="4">OCSGEc (G1)</th><th colspan="4">OCSGEu (G2)</th><th colspan="4">MOS (M)</th><th rowspan="2">Average</th></tr><tr><th>C</th><th>O</th><th>G1</th><th>G2</th><th>M</th><th>P</th><th>O</th><th>G1</th><th>G2</th><th>M</th><th>P</th><th>C</th><th>G1</th><th>G2</th><th>M</th><th>P</th><th>C</th><th>O</th><th>G1</th><th>P</th><th>C</th><th>O</th><th>G2</th><th>P</th><th>C</th><th>O</th><th>G1</th></tr></thead><tbody><tr><td rowspan="2">Early</td><td>SourceSpe</td><td>64</td><td>60</td><td>73</td><td>82</td><td>82</td><td>75</td><td>62</td><td>75</td><td>83</td><td>83</td><td>74</td><td>64</td><td>78</td><td>85</td><td>85</td><td>70</td><td>57</td><td>59</td><td>89</td><td>69</td><td>55</td><td>56</td><td>79</td><td>86</td><td>84</td><td>66</td></tr><tr><td>Unique</td><td>65</td><td>63</td><td>75</td><td>84</td><td>84</td><td>76</td><td>64</td><td>77</td><td>85</td><td><strong>85</strong></td><td>73</td><td>65</td><td>79</td><td>86</td><td>86</td><td>70</td><td>59</td><td>61</td><td>91</td><td>70</td><td>57</td><td>59</td><td>82</td><td>87</td><td>84</td><td>67</td><td>74</td></tr><tr><td rowspan="2">Mid-1</td><td>SourceSpe</td><td>65</td><td>62</td><td>76</td><td>83</td><td>84</td><td>77</td><td>65</td><td>77</td><td>85</td><td>84</td><td>73</td><td>66</td><td>80</td><td>87</td><td>86</td><td>71</td><td>59</td><td>60</td><td>90</td><td>71</td><td>57</td><td>59</td><td>82</td><td>86</td><td>84</td><td>66</td><td>74</td></tr><tr><td>TargetSpe</td><td>64</td><td>64</td><td>75</td><td>83</td><td>84</td><td><strong>77</strong></td><td>65</td><td>76</td><td>84</td><td><strong>85</strong></td><td>74</td><td>64</td><td>80</td><td>87</td><td>86</td><td>69</td><td>59</td><td>61</td><td>92</td><td>71</td><td>58</td><td>58</td><td>81</td><td><strong>87</strong></td><td>83</td><td>67</td><td>74</td></tr><tr><td rowspan="2">Mid-2</td><td>Unique</td><td>65</td><td>62</td><td>75</td><td>84</td><td>83</td><td>75</td><td>64</td><td>77</td><td>84</td><td>84</td><td>74</td><td>66</td><td>79</td><td>86</td><td>86</td><td>71</td><td>59</td><td>61</td><td>90</td><td>70</td><td>56</td><td>58</td><td>80</td><td><strong>87</strong></td><td><strong>85</strong></td><td>67</td><td>74</td></tr><tr><td>SourceSpe</td><td>66</td><td>63</td><td>74</td><td>83</td><td>83</td><td>76</td><td>64</td><td>77</td><td>83</td><td>84</td><td>73</td><td>67</td><td>79</td><td>85</td><td>86</td><td>70</td><td>60</td><td>61</td><td>90</td><td>71</td><td>57</td><td>58</td><td>80</td><td><strong>87</strong></td><td>84</td><td>67</td><td>74</td></tr><tr><td rowspan="2">Late</td><td>TargetSpe</td><td>66</td><td>62</td><td>75</td><td>84</td><td>82</td><td>75</td><td>63</td><td>78</td><td>83</td><td><strong>85</strong></td><td>74</td><td>65</td><td>80</td><td>86</td><td>86</td><td>72</td><td>60</td><td>62</td><td>91</td><td>70</td><td>55</td><td>57</td><td>79</td><td><strong>86</strong></td><td><strong>85</strong></td><td>67</td><td>74</td></tr><tr><td>Unique</td><td>66</td><td>64</td><td>75</td><td>84</td><td>84</td><td>76</td><td>65</td><td>77</td><td>85</td><td><strong>85</strong></td><td>73</td><td>66</td><td>79</td><td>87</td><td>86</td><td>71</td><td>59</td><td>62</td><td>92</td><td>71</td><td>57</td><td>60</td><td>82</td><td><strong>87</strong></td><td><strong>85</strong></td><td>67</td><td>75</td></tr><tr><td rowspan="2">Mid-3</td><td>SourceSpe</td><td>66</td><td>63</td><td>76</td><td>85</td><td>83</td><td>76</td><td>64</td><td>77</td><td>85</td><td>86</td><td>74</td><td>67</td><td>81</td><td>86</td><td>86</td><td>69</td><td>59</td><td>61</td><td>92</td><td>70</td><td>57</td><td>59</td><td>81</td><td><strong>87</strong></td><td><strong>85</strong></td><td>66</td><td>75</td></tr><tr><td>TargetSpe</td><td>64</td><td>62</td><td>76</td><td>85</td><td>84</td><td>76</td><td>64</td><td>78</td><td>84</td><td>84</td><td>75</td><td>65</td><td>81</td><td>86</td><td>86</td><td>69</td><td>59</td><td>62</td><td>92</td><td>70</td><td>58</td><td>58</td><td>83</td><td><strong>86</strong></td><td><strong>85</strong></td><td>66</td><td>75</td></tr><tr><td rowspan="2">Late</td><td>Unique</td><td><strong>70</strong></td><td>68</td><td><strong>79</strong></td><td><strong>86</strong></td><td><strong>85</strong></td><td><strong>78</strong></td><td><strong>69</strong></td><td><strong>79</strong></td><td><strong>86</strong></td><td><strong>85</strong></td><td><strong>77</strong></td><td><strong>70</strong></td><td><strong>82</strong></td><td><strong>88</strong></td><td><strong>87</strong></td><td><strong>73</strong></td><td><strong>63</strong></td><td><strong>65</strong></td><td><strong>93</strong></td><td><strong>73</strong></td><td>62</td><td><strong>63</strong></td><td><strong>85</strong></td><td><strong>87</strong></td><td><strong>85</strong></td><td><strong>69</strong></td><td><strong>77</strong></td></tr><tr><td>SourceSpe</td><td><strong>70</strong></td><td><strong>69</strong></td><td><strong>79</strong></td><td><strong>86</strong></td><td><strong>85</strong></td><td><strong>78</strong></td><td><strong>69</strong></td><td><strong>79</strong></td><td><strong>86</strong></td><td><strong>85</strong></td><td><strong>76</strong></td><td><strong>70</strong></td><td><strong>82</strong></td><td><strong>88</strong></td><td>86</td><td>72</td><td><strong>63</strong></td><td>64</td><td>92</td><td>72</td><td>62</td><td>62</td><td>84</td><td><strong>87</strong></td><td>84</td><td>68</td><td><strong>77</strong></td></tr><tr><td rowspan="2">Late</td><td>TargetSpe</td><td>69</td><td>67</td><td>78</td><td><strong>86</strong></td><td><strong>85</strong></td><td><strong>78</strong></td><td>68</td><td>78</td><td><strong>86</strong></td><td><strong>85</strong></td><td>76</td><td><strong>70</strong></td><td><strong>82</strong></td><td><strong>88</strong></td><td>86</td><td>72</td><td>62</td><td><strong>65</strong></td><td><strong>93</strong></td><td>72</td><td>62</td><td>62</td><td>84</td><td><strong>86</strong></td><td><strong>85</strong></td><td>68</td><td><strong>77</strong></td></tr></tbody></table>

<table><caption>Table I.2: Comparison of various Fusion strategies using DEM+Aspect, our 6 land cover maps and the MLCT-Net in terms of OA<sub>ag</sub>.</caption><thead><tr><th rowspan="2">Source</th><th rowspan="2">Target</th><th colspan="4">CGLS (P)</th><th colspan="4">CLC (C)</th><th colspan="4">OSO (O)</th><th colspan="4">OCSGEc (G1)</th><th colspan="4">OCSGEu (G2)</th><th colspan="4">MOS (M)</th><th rowspan="2">Average</th></tr><tr><th>C</th><th>O</th><th>G1</th><th>G2</th><th>M</th><th>P</th><th>O</th><th>G1</th><th>G2</th><th>M</th><th>P</th><th>C</th><th>G1</th><th>G2</th><th>M</th><th>P</th><th>C</th><th>O</th><th>G1</th><th>P</th><th>C</th><th>O</th><th>G2</th><th>P</th><th>C</th><th>O</th><th>G1</th></tr></thead><tbody><tr><td rowspan="2">Early</td><td>SourceSpe</td><td>59</td><td>54</td><td>69</td><td>78</td><td>78</td><td>71</td><td>57</td><td>72</td><td>80</td><td>80</td><td>75</td><td>65</td><td>78</td><td>85</td><td>85</td><td>70</td><td>57</td><td>57</td><td>91</td><td>70</td><td>54</td><td>52</td><td>80</td><td>86</td><td>84</td><td>64</td><td>71</td></tr><tr><td>Unique</td><td>60</td><td>56</td><td>70</td><td>80</td><td>80</td><td>72</td><td>57</td><td>73</td><td>81</td><td>81</td><td>75</td><td>65</td><td>78</td><td>85</td><td>85</td><td>71</td><td>58</td><td>56</td><td>91</td><td>70</td><td>54</td><td>52</td><td>79</td><td>86</td><td>83</td><td>64</td><td>72</td></tr><tr><td rowspan="2">Mid-1</td><td>SourceSpe</td><td>60</td><td>57</td><td>70</td><td>80</td><td>80</td><td>73</td><td>58</td><td>73</td><td>81</td><td>81</td><td>75</td><td>65</td><td>78</td><td>85</td><td>85</td><td>71</td><td>58</td><td>56</td><td>91</td><td>70</td><td>54</td><td>52</td><td>79</td><td>86</td><td>83</td><td>64</td><td>72</td></tr><tr><td>TargetSpe</td><td>60</td><td>57</td><td>70</td><td>80</td><td>80</td><td>73</td><td>58</td><td>73</td><td>81</td><td>81</td><td>75</td><td>65</td><td>78</td><td>85</td><td>85</td><td>71</td><td>58</td><td>56</td><td>91</td><td>70</td><td>54</td><td>52</td><td>79</td><td>86</td><td>83</td><td>64</td><td>72</td></tr><tr><td rowspan="2">Mid-2</td><td>Unique</td><td>60</td><td>56</td><td>70</td><td>80</td><td>80</td><td>72</td><td>57</td><td>73</td><td>81</td><td>81</td><td>75</td><td>66</td><td>78</td><td>85</td><td>85</td><td>71</td><td>58</td><td>56</td><td>91</td><td>70</td><td>54</td><td>52</td><td>79</td><td>86</td><td>83</td><td>64</td><td>72</td></tr><tr><td>SourceSpe</td><td>60</td><td>57</td><td>70</td><td>80</td><td>80</td><td>73</td><td>58</td><td>73</td><td>81</td><td>81</td><td>75</td><td>65</td><td>78</td><td>85</td><td>85</td><td>71</td><td>59</td><td>56</td><td>91</td><td>70</td><td>54</td><td>52</td><td>79</td><td>86</td><td>83</td><td>64</td><td>72</td></tr><tr><td rowspan="2">Late</td><td>TargetSpe</td><td>60</td><td>57</td><td>70</td><td>80</td><td>80</td><td>73</td><td>58</td><td>73</td><td>81</td><td>81</td><td>75</td><td>65</td><td>78</td><td>85</td><td>85</td><td>71</td><td>58</td><td>56</td><td>91</td><td>70</td><td>54</td><td>52</td><td>79</td><td>86</td><td>83</td><td>64</td><td>72</td></tr><tr><td>Unique</td><td>61</td><td>58</td><td>71</td><td>80</td><td>80</td><td>73</td><td>59</td><td>73</td><td>82</td><td>82</td><td>75</td><td>65</td><td>79</td><td>85</td><td>85</td><td>71</td><td>58</td><td>58</td><td>92</td><td>71</td><td>56</td><td>54</td><td>81</td><td>86</td><td>84</td><td>64</td><td>72</td></tr><tr><td rowspan="2">Mid-3</td><td>SourceSpe</td><td>61</td><td>57</td><td>71</td><td>80</td><td>80</td><td>72</td><td>59</td><td>73</td><td>82</td><td>82</td><td>75</td><td>66</td><td>79</td><td>85</td><td>85</td><td>71</td><td>59</td><td>58</td><td>92</td><td>71</td><td>56</td><td>54</td><td>81</td><td>86</td><td>84</td><td>64</td><td>72</td></tr><tr><td>TargetSpe</td><td>60</td><td>57</td><td>71</td><td>80</td><td>80</td><td>73</td><td>59</td><td>73</td><td>82</td><td>82</td><td>75</td><td>65</td><td>79</td><td>85</td><td>85</td><td>71</td><td>58</td><td>58</td><td>92</td><td>71</td><td>56</td><td>54</td><td>81</td><td>86</td><td>84</td><td>64</td><td>72</td></tr><tr><td rowspan="2">Late</td><td>Unique</td><td>67</td><td>60</td><td>72</td><td>80</td><td>81</td><td>75</td><td>60</td><td>74</td><td>81</td><td>82</td><td>79</td><td>69</td><td>80</td><td>86</td><td>86</td><td>74</td><td>61</td><td>60</td><td>93</td><td>73</td><td>59</td><td>55</td><td>81</td><td>87</td><td>84</td><td>63</td><td>74</td></tr><tr><td>SourceSpe</td><td>67</td><td>59</td><td>72</td><td>80</td><td>80</td><td>75</td><td>60</td><td>74</td><td>81</td><td>82</td><td>79</td><td>68</td><td>81</td><td>86</td><td>86</td><td>74</td><td>61</td><td>60</td><td>93</td><td>73</td><td>59</td><td>55</td><td>81</td><td>87</td><td>84</td><td>63</td><td><strong>74</strong></td></tr><tr><td rowspan="2">Late</td><td>TargetSpe</td><td>67</td><td>60</td><td>72</td><td>80</td><td>81</td><td>75</td><td>60</td><td>74</td><td>81</td><td>82</td><td>79</td><td>68</td><td>80</td><td>86</td><td>86</td><td>74</td><td>61</td><td>60</td><td>93</td><td>73</td><td>60</td><td>55</td><td>81</td><td>87</td><td>84</td><td>63</td><td><strong>74</strong></td></tr></tbody></table>
Visual representation of SRS obtained with BoW and Word2Vec
<figure><img src="image_116.png" /><figcaption>Figure J.1: PCA representation of the HDSRS obtained with a trained on generic text corpora Word2Vec.</figcaption></figure>
<figure><img src="image_117.png" /><figcaption>Figure J.2: TSNE representation of the HDSRS obtained with a trained on generic text corpora Word2Vec.</figcaption></figure>
<figure><img src="image_118.png" /><figcaption>Figure J.3: PCA representation of the HDSRS obtained with a trained on LCDD BoW. OSO is here the test map, unseen when building the dictionary.</figcaption></figure>
<figure><img src="image_119.png" /><figcaption>Figure J.4: TSNE representation of the HDSRS obtained with a trained on LCDD BoW.
OSO is here the test map, unseen when building the dictionary.</figcaption></figure>