Commit 45e61cab authored by Romulo Pereira Goncalves's avatar Romulo Pereira Goncalves
Browse files

Remove dependency on pinned version for rgdal, sp an sf

parent 81b17efe
......@@ -8,6 +8,9 @@ output:
toc: true
toc_depth: 2
variant: gfm
pdf_document:
toc: true
toc_depth: 2
html_document:
theme: united
highlight: tango
......@@ -17,9 +20,6 @@ output:
collapsed: false
smooth_scroll: false
df_print: paged
pdf_document:
toc: true
toc_depth: 2
always_allow_html: yes
header-includes:
- \usepackage{caption}
......@@ -36,7 +36,7 @@ knitr::opts_chunk$set(tidy.opts = list(width.cutoff = 75), tidy = TRUE, fig.pos
# 1 Introduction
This manual introduces the Habitat Sampler (HaSa), an innovative tool that autonomously generates representative reference samples for predictive modelling of surface class probabilities. The tool can be applied to any image data that displays surface structures and dynamics of any kind at multiple spatial and temporal scales. HaSa was initially developed to classify habitat dynamics in semi-natural ecosystems but the procedure can theoretically be applied to any surface. The main innovation of the tool is that it reduces reliance on comprehensive in situ ground truth data or comprehensive training datasets which constrain accurate image classification particularly in complex scenes.
Though development of HaSa has prioritized ease of use, this documentation assume a familiarity with the R software. The document is built successively and is intended to lead you step-by-step through the HaSa procedure of generating probability and classification maps. HaSa is still in development and any suggestions or improvements are welcomed and encouraged in our [GitLab Community Version](https://git.gfz-potsdam.de/habitat-sampler/HabitatSampler.git). If questions remain please don't hesitate to contact the authors of the package. For a detailed description of the Habitat Sampler and its applications, see [Neumann et al., (2020)](https://doi.org/10.1111/ddi.13165).
Though development of HaSa has prioritized ease of use, this documentation assume a familiarity with the R software. The document is built successively and is intended to lead you step-by-step through the HaSa procedure of generating probability and classification maps. HaSa is still in development and any suggestions or improvements are welcomed and encouraged in our [GitHub Community Version](https://git.gfz-potsdam.de/habitat-sampler/HabitatSampler.git). If questions remain please don't hesitate to contact the authors of the package. For a detailed description of the Habitat Sampler and its applications, see [Neumann et al., (2020)](https://doi.org/10.1111/ddi.13165).
## 1.1 Usage
The tool is implemented in R and uses Leaflet [(Cheng et al., 2019)](https://rdrr.io/cran/leaflet/) to generate interactive maps in a web browser. There are no assumptions about the input image data and there are no constraints for the spectral-temporal-spatial domain in which the image is sampled. The tool requires the input of a priori expert user knowledge to generate reference data about expected surface classes which are delineated in the imagery or extracted from an external spectral library. The user has the choice between image classifiers [random forest](https://doi.org/10.1023/A:1010933404324) (RF) and [support vector](https://doi.org/10.1145/130385.130401) (SV).
......@@ -66,19 +66,13 @@ The point shapefile contains a point location per class and is used to extract t
The following procedure will lead you through the preliminary steps required to setup the HaSa tool.
## 2.1 HaSa dependencies
HaSa uses the latest version of the `velox` library (`v0.2.0`) which does not compile with the latest version the interface to the C++ Boost library `BH`. Hence, it is necessary to pin the `BH` version. HaSa does not yet support the latest developments in `rgdal` and `sp` related with projections. It is also necessary to pin the versions for `sf`, `sp` and `rgdal`.
HaSa uses the latest version of the `velox` library (`v0.2.0`) which does not compile with the latest version the interface to the C++ Boost library `BH`. Hence, it is necessary to pin the `BH` version.
The installation of `BH`, `sf`, `sp` and `rgdal` is possible with the following commands:
```{r install dependencies, eval = FALSE}
install.packages("remotes")
install.packages("https://cran.r-project.org/src/contrib/Archive/BH/BH_1.69.0-1.tar.
gz", repos = NULL, type = "source")
install.packages("https://cran.r-project.org/src/contrib/Archive/sf/sf_0.9-0.tar.
gz", repos = NULL, type = "source")
install.packages("https://cran.r-project.org/src/contrib/Archive/sp/sp_1.4-4.tar.
gz", repos = NULL, type = "source")
install.packages("https://cran.r-project.org/src/contrib/Archive/rgdal/rgdal_1.5-12.
tar.gz", repos = NULL, type = "source")
```
## 2.2 Install HaSa
......
......@@ -2871,7 +2871,7 @@ div.tocify {
<div id="introduction" class="section level1">
<h1>1 Introduction</h1>
<p>This manual introduces the Habitat Sampler (HaSa), an innovative tool that autonomously generates representative reference samples for predictive modelling of surface class probabilities. The tool can be applied to any image data that displays surface structures and dynamics of any kind at multiple spatial and temporal scales. HaSa was initially developed to classify habitat dynamics in semi-natural ecosystems but the procedure can theoretically be applied to any surface. The main innovation of the tool is that it reduces reliance on comprehensive in situ ground truth data or comprehensive training datasets which constrain accurate image classification particularly in complex scenes.</p>
<p>Though development of HaSa has prioritized ease of use, this documentation assume a familiarity with the R software. The document is built successively and is intended to lead you step-by-step through the HaSa procedure of generating probability and classification maps. HaSa is still in development and any suggestions or improvements are welcomed and encouraged in our <a href="https://git.gfz-potsdam.de/habitat-sampler/HabitatSampler.git">GitLab Community Version</a>. If questions remain please don’t hesitate to contact the authors of the package. For a detailed description of the Habitat Sampler and its applications, see <a href="https://doi.org/10.1111/ddi.13165">Neumann et al., (2020)</a>.</p>
<p>Though development of HaSa has prioritized ease of use, this documentation assume a familiarity with the R software. The document is built successively and is intended to lead you step-by-step through the HaSa procedure of generating probability and classification maps. HaSa is still in development and any suggestions or improvements are welcomed and encouraged in our <a href="https://git.gfz-potsdam.de/habitat-sampler/HabitatSampler.git">GitHub Community Version</a>. If questions remain please don’t hesitate to contact the authors of the package. For a detailed description of the Habitat Sampler and its applications, see <a href="https://doi.org/10.1111/ddi.13165">Neumann et al., (2020)</a>.</p>
<div id="usage" class="section level2">
<h2>1.1 Usage</h2>
<p>The tool is implemented in R and uses Leaflet <a href="https://rdrr.io/cran/leaflet/">(Cheng et al., 2019)</a> to generate interactive maps in a web browser. There are no assumptions about the input image data and there are no constraints for the spectral-temporal-spatial domain in which the image is sampled. The tool requires the input of a priori expert user knowledge to generate reference data about expected surface classes which are delineated in the imagery or extracted from an external spectral library. The user has the choice between image classifiers <a href="https://doi.org/10.1023/A:1010933404324">random forest</a> (RF) and <a href="https://doi.org/10.1145/130385.130401">support vector</a> (SV).</p>
......@@ -2992,21 +2992,12 @@ div.tocify {
<p>The following procedure will lead you through the preliminary steps required to setup the HaSa tool.</p>
<div id="hasa-dependencies" class="section level2">
<h2>2.1 HaSa dependencies</h2>
<p>HaSa uses the latest version of the <code>velox</code> library (<code>v0.2.0</code>) which does not compile with the latest version the interface to the C++ Boost library <code>BH</code>. Hence, it is necessary to pin the <code>BH</code> version. HaSa does not yet support the latest developments in <code>rgdal</code> and <code>sp</code> related with projections. It is also necessary to pin the versions for <code>sf</code>, <code>sp</code> and <code>rgdal</code>.</p>
<p>HaSa uses the latest version of the <code>velox</code> library (<code>v0.2.0</code>) which does not compile with the latest version the interface to the C++ Boost library <code>BH</code>. Hence, it is necessary to pin the <code>BH</code> version.</p>
<p>The installation of <code>BH</code>, <code>sf</code>, <code>sp</code> and <code>rgdal</code> is possible with the following commands:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1"></a><span class="kw">install.packages</span>(<span class="st">&quot;remotes&quot;</span>)</span>
<span id="cb1-2"><a href="#cb1-2"></a><span class="kw">install.packages</span>(<span class="st">&quot;https://cran.r-project.org/src/contrib/Archive/BH/BH_1.69.0-1.tar.</span></span>
<span id="cb1-3"><a href="#cb1-3"></a><span class="st"> gz&quot;</span>, </span>
<span id="cb1-4"><a href="#cb1-4"></a> <span class="dt">repos =</span> <span class="ot">NULL</span>, <span class="dt">type =</span> <span class="st">&quot;source&quot;</span>)</span>
<span id="cb1-5"><a href="#cb1-5"></a><span class="kw">install.packages</span>(<span class="st">&quot;https://cran.r-project.org/src/contrib/Archive/sf/sf_0.9-0.tar.</span></span>
<span id="cb1-6"><a href="#cb1-6"></a><span class="st"> gz&quot;</span>, </span>
<span id="cb1-7"><a href="#cb1-7"></a> <span class="dt">repos =</span> <span class="ot">NULL</span>, <span class="dt">type =</span> <span class="st">&quot;source&quot;</span>)</span>
<span id="cb1-8"><a href="#cb1-8"></a><span class="kw">install.packages</span>(<span class="st">&quot;https://cran.r-project.org/src/contrib/Archive/sp/sp_1.4-4.tar.</span></span>
<span id="cb1-9"><a href="#cb1-9"></a><span class="st"> gz&quot;</span>, </span>
<span id="cb1-10"><a href="#cb1-10"></a> <span class="dt">repos =</span> <span class="ot">NULL</span>, <span class="dt">type =</span> <span class="st">&quot;source&quot;</span>)</span>
<span id="cb1-11"><a href="#cb1-11"></a><span class="kw">install.packages</span>(<span class="st">&quot;https://cran.r-project.org/src/contrib/Archive/rgdal/rgdal_1.5-12.</span></span>
<span id="cb1-12"><a href="#cb1-12"></a><span class="st"> tar.gz&quot;</span>, </span>
<span id="cb1-13"><a href="#cb1-13"></a> <span class="dt">repos =</span> <span class="ot">NULL</span>, <span class="dt">type =</span> <span class="st">&quot;source&quot;</span>)</span></code></pre></div>
<span id="cb1-4"><a href="#cb1-4"></a> <span class="dt">repos =</span> <span class="ot">NULL</span>, <span class="dt">type =</span> <span class="st">&quot;source&quot;</span>)</span></code></pre></div>
</div>
<div id="install-hasa" class="section level2">
<h2>2.2 Install HaSa</h2>
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