Wie zeige ich die erste Folie einer Web-Diashow an, die Bilder UND begleitende Beschreibungen in Kästchen enthält? ⇐ CSS

[Anonymous]

Dies ist das erste Bild der Diashow. Die ganze Hilfe, die ich online zu finden versucht habe, betraf nur Bild-Diashows, aber meine Diashow enthält Bilder UND Beschreibungen in Kästchen über dem Bild. Dies ist beispielsweise die erste Folie:


Zunächst sind alle Folien ausgeblendet. Nachdem dann alles geladen ist, soll die erste Folie erscheinen. Es erscheint jedoch NICHT. Hier ist der relevante Code. Was mache ich falsch?

Code: Select all

const slides = document.querySelectorAll("slide);")
let slideIndex = 0;
let intervalId = null;

document.addEventListener("DOMContentLoaded", initializeSlider);

function initializeSlider(){

slides[slideIndex].classList.add("displaySlide");

}
function showSlide(index){

}
function prevSlide(){

}
function nextSlide(){

}

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.slideshow-equipment{
background-color: #A3A3A3;
width: 365px;
height: 454px;
margin-left: 100px;
}
.slideshow-process{
height: 800px;
object-fit: cover;
}
.slider{
height: 800px;
position: relative;
}
.slide{
display: none;
}
.displaySlider{
display: block;
}
.box-process{
position: absolute;
width: 575px;
height: 175px;
background-color: #0D0D0D;
opacity: 100%;
left: 100px;
top: 100px;
padding: 20px;
}
.title-box-process{
font-size: 18px;
font-weight: 700;
}

Code: Select all

        





[img]images/satelite view.jpg[/img]



1. Area Selection

We target areas ranging from county-sized regions to multi-county basins (up to five counties) for comprehensive analysis, ensuring sufficient geological variability for robust predictions.



[img]images/variable definition.jpg[/img]



2. Variable Definition

We select an equal number of known oil wells and dry holes within the target area to establish critical training variables, balancing positive and negative data points.



[img]images/training layer.jpg[/img]



3. Training Layer Development

Using Landsat9, Sentinel2, and airborne LiDAR, we create detailed training layers that capture geological and topographical features.



[img]images/data training.jpg[/img]



4.  Data Training and Pattern Recognition

Our machine learning models are trained to identify subtle patterns and trends indicative of subsurface oil accumulations, leveraging the predictive power of Random Forest algorithms.



[img]images/map product.jpg[/img]



5. Map Production

We utilize ArcGIS Pro's “predict to raster” functionality to generate high-resolution, visually intuitive maps that highlight areas of high petroleum potential.





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