diff --git a/Benchmark_files/refs.bib b/Benchmark_files/refs.bib
deleted file mode 100644
index ae695f7..0000000
--- a/Benchmark_files/refs.bib
+++ /dev/null
@@ -1,14 +0,0 @@
-@article{Wang2017,
-    author = {Wang, Yinsong AND Zou, Yajie AND Henrickson, Kristian AND Wang, Yinhai AND Tang, Jinjun AND Park, Byung-Jung},
-    journal = {PLOS ONE},
-    publisher = {Public Library of Science},
-    title = {Google Earth elevation data extraction and accuracy assessment for transportation applications},
-    year = {2017},
-    month = {04},
-    volume = {12},
-    url = {https://doi.org/10.1371/journal.pone.0175756},
-    pages = {1-17},
-    abstract = {Roadway elevation data is critical for a variety of transportation analyses. However, it has been challenging to obtain such data and most roadway GIS databases do not have them. This paper intends to address this need by proposing a method to extract roadway elevation data from Google Earth (GE) for transportation applications. A comprehensive accuracy assessment of the GE-extracted elevation data is conducted for the area of conterminous USA. The GE elevation data was compared with the ground truth data from nationwide GPS benchmarks and roadway monuments from six states in the conterminous USA. This study also compares the GE elevation data with the elevation raster data from the U.S. Geological Survey National Elevation Dataset (USGS NED), which is a widely used data source for extracting roadway elevation. Mean absolute error (MAE) and root mean squared error (RMSE) are used to assess the accuracy and the test results show MAE, RMSE and standard deviation of GE roadway elevation error are 1.32 meters, 2.27 meters and 2.27 meters, respectively. Finally, the proposed extraction method was implemented and validated for the following three scenarios: (1) extracting roadway elevation differentiating by directions, (2) multi-layered roadway recognition in freeway segment and (3) slope segmentation and grade calculation in freeway segment. The methodology validation results indicate that the proposed extraction method can locate the extracting route accurately, recognize multi-layered roadway section, and segment the extracted route by grade automatically. Overall, it is found that the high accuracy elevation data available from GE provide a reliable data source for various transportation applications.},
-    number = {4},
-    doi = {10.1371/journal.pone.0175756}
-}
\ No newline at end of file
diff --git a/raster/PortoCOPERNICUS_clip.tif b/raster/PortoCOPERNICUS_clip.tif
new file mode 100644
index 0000000..a531c6a
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diff --git a/raster/PortoCOPERNICUS_clip.tif.aux.xml b/raster/PortoCOPERNICUS_clip.tif.aux.xml
new file mode 100644
index 0000000..d848d79
--- /dev/null
+++ b/raster/PortoCOPERNICUS_clip.tif.aux.xml
@@ -0,0 +1,17 @@
+<PAMDataset>
+  <Metadata>
+    <MDI key="DataType">Elevation</MDI>
+  </Metadata>
+  <PAMRasterBand band="1">
+    <Description>Band_1</Description>
+    <Metadata>
+      <MDI key="BandName">Band_1</MDI>
+      <MDI key="RepresentationType">ATHEMATIC</MDI>
+      <MDI key="STATISTICS_MAXIMUM">283.01803588867</MDI>
+      <MDI key="STATISTICS_MEAN">85.473022181853</MDI>
+      <MDI key="STATISTICS_MINIMUM">-0.37667798995972</MDI>
+      <MDI key="STATISTICS_STDDEV">44.964519526224</MDI>
+      <MDI key="STATISTICS_VALID_PERCENT">78.89</MDI>
+    </Metadata>
+  </PAMRasterBand>
+</PAMDataset>
diff --git a/shapefiles/ConcelhosPT.gpkg b/shapefiles/ConcelhosPT.gpkg
index 0c89543..bcac171 100644
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diff --git a/shapefiles/RedeViariaPorto_declives.gpkg b/shapefiles/RedeViariaPorto_declives.gpkg
index cf75b17..25446d2 100644
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